{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "54afd431",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Example usage:\n",
    "# m, f = convert_simple_to_problem_format(\"path/to/simple_format_file.txt\")\n",
    "# selected_facilities, total_cost = solve_pyomo(m, f)\n",
    "# m, f = convert_orlib_cap_to_problem_format(\"/Users/krupke/Repositories/HardProblemBench/tasks/facility_location/CLSC/3133GapCS.txt\")\n",
    "# m,f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e33e0022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'result': {'objective': 10925.0, 'bound': 6919.0},\n",
       " 'timestamp': '2025-06-06T21:40:36.766082',\n",
       " 'runtime': 60.287017583847046,\n",
       " 'stdout': [],\n",
       " 'stderr': [],\n",
       " 'logging': [],\n",
       " 'env_fingerprint': '910f97a9d06f0ae029ebb09906d05e63b1c896db',\n",
       " 'args_fingerprint': '0c1d843a373506211d47aba342d4b1a3e35d3065',\n",
       " 'parameters': {'func': 'solve_via_add_circuit',\n",
       "  'args': {'vehicle_capacity': 15, 'instance_name': 'instance_0'}},\n",
       " 'argv': ['benchmark.py'],\n",
       " 'env': {'hostname': 'manjaro-w7',\n",
       "  'python_version': '3.12.2 | packaged by conda-forge | (main, Feb 16 2024, 20:50:58) [GCC 12.3.0]',\n",
       "  'python': '/home/krupke/anaconda3/envs/mo312/bin/python',\n",
       "  'cwd': '/home/krupke/Repositories/cpsat-primer/examples/cvrp',\n",
       "  'git_revision': '94e1224d67610f9114057afd4bc504f8786e4ad8',\n",
       "  'python_file': '/home/krupke/Repositories/cpsat-primer/examples/cvrp/benchmark.py'}}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from algbench import Benchmark, read_as_pandas\n",
    "\n",
    "benchmark = Benchmark(\"./.data\", hide_output=False)\n",
    "benchmark.front()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d8d593fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instance_name</th>\n",
       "      <th>model</th>\n",
       "      <th>time</th>\n",
       "      <th>objective</th>\n",
       "      <th>bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>instance_0</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.287018</td>\n",
       "      <td>10925.0</td>\n",
       "      <td>6919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>instance_0</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.335490</td>\n",
       "      <td>10925.0</td>\n",
       "      <td>5587.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>instance_0</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>61.253904</td>\n",
       "      <td>10928.0</td>\n",
       "      <td>5565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>instance_1</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.456329</td>\n",
       "      <td>17784.0</td>\n",
       "      <td>10291.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>instance_1</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.091813</td>\n",
       "      <td>17868.0</td>\n",
       "      <td>5889.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>instance_1</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.072893</td>\n",
       "      <td>17572.0</td>\n",
       "      <td>5651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>instance_2</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.539853</td>\n",
       "      <td>16296.0</td>\n",
       "      <td>8395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>instance_2</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.451678</td>\n",
       "      <td>15922.0</td>\n",
       "      <td>6630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>instance_2</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.175198</td>\n",
       "      <td>15797.0</td>\n",
       "      <td>6516.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>instance_3</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.355911</td>\n",
       "      <td>11976.0</td>\n",
       "      <td>7341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>instance_3</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.068853</td>\n",
       "      <td>12151.0</td>\n",
       "      <td>5167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>instance_3</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.051112</td>\n",
       "      <td>11868.0</td>\n",
       "      <td>5095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>instance_4</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.406408</td>\n",
       "      <td>11577.0</td>\n",
       "      <td>8183.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>instance_4</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.081246</td>\n",
       "      <td>11606.0</td>\n",
       "      <td>5929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>instance_4</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.078709</td>\n",
       "      <td>11668.0</td>\n",
       "      <td>5937.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>instance_5</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.603206</td>\n",
       "      <td>22431.0</td>\n",
       "      <td>13145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>instance_5</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.117246</td>\n",
       "      <td>22498.0</td>\n",
       "      <td>6537.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>instance_5</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.135339</td>\n",
       "      <td>21943.0</td>\n",
       "      <td>6407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>instance_6</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.311083</td>\n",
       "      <td>10552.0</td>\n",
       "      <td>6841.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>instance_6</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.061738</td>\n",
       "      <td>10484.0</td>\n",
       "      <td>5576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>instance_6</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.047723</td>\n",
       "      <td>10484.0</td>\n",
       "      <td>5287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>instance_7</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.650535</td>\n",
       "      <td>13912.0</td>\n",
       "      <td>7481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>instance_7</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.099457</td>\n",
       "      <td>14484.0</td>\n",
       "      <td>5866.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>instance_7</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.096410</td>\n",
       "      <td>13846.0</td>\n",
       "      <td>5612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>instance_8</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.560840</td>\n",
       "      <td>23634.0</td>\n",
       "      <td>13601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>instance_8</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.112117</td>\n",
       "      <td>23309.0</td>\n",
       "      <td>6504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>instance_8</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.112612</td>\n",
       "      <td>23277.0</td>\n",
       "      <td>6201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>instance_9</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.294591</td>\n",
       "      <td>11300.0</td>\n",
       "      <td>7250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>instance_9</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.058786</td>\n",
       "      <td>10966.0</td>\n",
       "      <td>5598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>instance_9</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.259737</td>\n",
       "      <td>10966.0</td>\n",
       "      <td>5339.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>instance_10</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.460354</td>\n",
       "      <td>13378.0</td>\n",
       "      <td>7427.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>instance_10</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.189703</td>\n",
       "      <td>13396.0</td>\n",
       "      <td>5696.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>instance_10</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.116284</td>\n",
       "      <td>13364.0</td>\n",
       "      <td>5666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>instance_11</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.634087</td>\n",
       "      <td>18735.0</td>\n",
       "      <td>10413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>instance_11</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.106913</td>\n",
       "      <td>18542.0</td>\n",
       "      <td>6583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>instance_11</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.100442</td>\n",
       "      <td>18453.0</td>\n",
       "      <td>6279.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>instance_12</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.268100</td>\n",
       "      <td>10943.0</td>\n",
       "      <td>7252.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>instance_12</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.040003</td>\n",
       "      <td>10815.0</td>\n",
       "      <td>5886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>instance_12</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.237070</td>\n",
       "      <td>10765.0</td>\n",
       "      <td>5720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>instance_13</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.374117</td>\n",
       "      <td>10410.0</td>\n",
       "      <td>7455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>instance_13</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.092983</td>\n",
       "      <td>10335.0</td>\n",
       "      <td>5831.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>instance_13</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.070221</td>\n",
       "      <td>10103.0</td>\n",
       "      <td>5715.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>instance_14</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.486152</td>\n",
       "      <td>19866.0</td>\n",
       "      <td>8983.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>instance_14</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.277874</td>\n",
       "      <td>19816.0</td>\n",
       "      <td>6179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>instance_14</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.225131</td>\n",
       "      <td>19582.0</td>\n",
       "      <td>5961.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>instance_15</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.300826</td>\n",
       "      <td>14722.0</td>\n",
       "      <td>7628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>instance_15</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.052383</td>\n",
       "      <td>14656.0</td>\n",
       "      <td>5499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>instance_15</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.780100</td>\n",
       "      <td>14421.0</td>\n",
       "      <td>5446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>instance_16</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.475744</td>\n",
       "      <td>13102.0</td>\n",
       "      <td>8435.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>instance_16</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.250327</td>\n",
       "      <td>12611.0</td>\n",
       "      <td>6658.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>instance_16</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.070136</td>\n",
       "      <td>12604.0</td>\n",
       "      <td>6441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>instance_17</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.735184</td>\n",
       "      <td>20351.0</td>\n",
       "      <td>10078.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>instance_17</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.448105</td>\n",
       "      <td>20005.0</td>\n",
       "      <td>6385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>instance_17</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.242083</td>\n",
       "      <td>20037.0</td>\n",
       "      <td>6313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>instance_18</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.563130</td>\n",
       "      <td>10950.0</td>\n",
       "      <td>6491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>instance_18</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.332640</td>\n",
       "      <td>11019.0</td>\n",
       "      <td>5068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>instance_18</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.730875</td>\n",
       "      <td>10685.0</td>\n",
       "      <td>4966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>instance_19</td>\n",
       "      <td>solve_via_add_circuit</td>\n",
       "      <td>60.451458</td>\n",
       "      <td>10390.0</td>\n",
       "      <td>6890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>instance_19</td>\n",
       "      <td>solve_via_multiple_circuits</td>\n",
       "      <td>60.090784</td>\n",
       "      <td>10286.0</td>\n",
       "      <td>5344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>instance_19</td>\n",
       "      <td>solve_via_mtz</td>\n",
       "      <td>60.081750</td>\n",
       "      <td>10269.0</td>\n",
       "      <td>5226.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instance_name                        model       time  objective    bound\n",
       "0     instance_0        solve_via_add_circuit  60.287018    10925.0   6919.0\n",
       "1     instance_0  solve_via_multiple_circuits  60.335490    10925.0   5587.0\n",
       "2     instance_0                solve_via_mtz  61.253904    10928.0   5565.0\n",
       "3     instance_1        solve_via_add_circuit  60.456329    17784.0  10291.0\n",
       "4     instance_1  solve_via_multiple_circuits  60.091813    17868.0   5889.0\n",
       "5     instance_1                solve_via_mtz  60.072893    17572.0   5651.0\n",
       "6     instance_2        solve_via_add_circuit  60.539853    16296.0   8395.0\n",
       "7     instance_2  solve_via_multiple_circuits  60.451678    15922.0   6630.0\n",
       "8     instance_2                solve_via_mtz  60.175198    15797.0   6516.0\n",
       "9     instance_3        solve_via_add_circuit  60.355911    11976.0   7341.0\n",
       "10    instance_3  solve_via_multiple_circuits  60.068853    12151.0   5167.0\n",
       "11    instance_3                solve_via_mtz  60.051112    11868.0   5095.0\n",
       "12    instance_4        solve_via_add_circuit  60.406408    11577.0   8183.0\n",
       "13    instance_4  solve_via_multiple_circuits  60.081246    11606.0   5929.0\n",
       "14    instance_4                solve_via_mtz  60.078709    11668.0   5937.0\n",
       "15    instance_5        solve_via_add_circuit  60.603206    22431.0  13145.0\n",
       "16    instance_5  solve_via_multiple_circuits  60.117246    22498.0   6537.0\n",
       "17    instance_5                solve_via_mtz  60.135339    21943.0   6407.0\n",
       "18    instance_6        solve_via_add_circuit  60.311083    10552.0   6841.0\n",
       "19    instance_6  solve_via_multiple_circuits  60.061738    10484.0   5576.0\n",
       "20    instance_6                solve_via_mtz  60.047723    10484.0   5287.0\n",
       "21    instance_7        solve_via_add_circuit  60.650535    13912.0   7481.0\n",
       "22    instance_7  solve_via_multiple_circuits  60.099457    14484.0   5866.0\n",
       "23    instance_7                solve_via_mtz  60.096410    13846.0   5612.0\n",
       "24    instance_8        solve_via_add_circuit  60.560840    23634.0  13601.0\n",
       "25    instance_8  solve_via_multiple_circuits  60.112117    23309.0   6504.0\n",
       "26    instance_8                solve_via_mtz  60.112612    23277.0   6201.0\n",
       "27    instance_9        solve_via_add_circuit  60.294591    11300.0   7250.0\n",
       "28    instance_9  solve_via_multiple_circuits  60.058786    10966.0   5598.0\n",
       "29    instance_9                solve_via_mtz  60.259737    10966.0   5339.0\n",
       "30   instance_10        solve_via_add_circuit  60.460354    13378.0   7427.0\n",
       "31   instance_10  solve_via_multiple_circuits  60.189703    13396.0   5696.0\n",
       "32   instance_10                solve_via_mtz  60.116284    13364.0   5666.0\n",
       "33   instance_11        solve_via_add_circuit  60.634087    18735.0  10413.0\n",
       "34   instance_11  solve_via_multiple_circuits  60.106913    18542.0   6583.0\n",
       "35   instance_11                solve_via_mtz  60.100442    18453.0   6279.0\n",
       "36   instance_12        solve_via_add_circuit  60.268100    10943.0   7252.0\n",
       "37   instance_12  solve_via_multiple_circuits  60.040003    10815.0   5886.0\n",
       "38   instance_12                solve_via_mtz  60.237070    10765.0   5720.0\n",
       "39   instance_13        solve_via_add_circuit  60.374117    10410.0   7455.0\n",
       "40   instance_13  solve_via_multiple_circuits  60.092983    10335.0   5831.0\n",
       "41   instance_13                solve_via_mtz  60.070221    10103.0   5715.0\n",
       "42   instance_14        solve_via_add_circuit  60.486152    19866.0   8983.0\n",
       "43   instance_14  solve_via_multiple_circuits  60.277874    19816.0   6179.0\n",
       "44   instance_14                solve_via_mtz  60.225131    19582.0   5961.0\n",
       "45   instance_15        solve_via_add_circuit  60.300826    14722.0   7628.0\n",
       "46   instance_15  solve_via_multiple_circuits  60.052383    14656.0   5499.0\n",
       "47   instance_15                solve_via_mtz  60.780100    14421.0   5446.0\n",
       "48   instance_16        solve_via_add_circuit  60.475744    13102.0   8435.0\n",
       "49   instance_16  solve_via_multiple_circuits  60.250327    12611.0   6658.0\n",
       "50   instance_16                solve_via_mtz  60.070136    12604.0   6441.0\n",
       "51   instance_17        solve_via_add_circuit  60.735184    20351.0  10078.0\n",
       "52   instance_17  solve_via_multiple_circuits  60.448105    20005.0   6385.0\n",
       "53   instance_17                solve_via_mtz  60.242083    20037.0   6313.0\n",
       "54   instance_18        solve_via_add_circuit  60.563130    10950.0   6491.0\n",
       "55   instance_18  solve_via_multiple_circuits  60.332640    11019.0   5068.0\n",
       "56   instance_18                solve_via_mtz  60.730875    10685.0   4966.0\n",
       "57   instance_19        solve_via_add_circuit  60.451458    10390.0   6890.0\n",
       "58   instance_19  solve_via_multiple_circuits  60.090784    10286.0   5344.0\n",
       "59   instance_19                solve_via_mtz  60.081750    10269.0   5226.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = read_as_pandas(\n",
    "    \"./.data\",\n",
    "    lambda x: {\n",
    "        \"instance_name\": x[\"parameters\"][\"args\"][\"instance_name\"],\n",
    "        \"model\": x[\"parameters\"][\"func\"],\n",
    "        \"time\": x[\"runtime\"],\n",
    "        \"objective\": x[\"result\"][\"objective\"],\n",
    "        \"bound\": x[\"result\"][\"bound\"],\n",
    "    },\n",
    ")\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74049c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nkpKSqlxAGaP8cFJXr16tdtvyr/+ll14y+g1L5mSodflMycnJBq+TlJSkt525lN9e3759cebMmVrflimPvbpkyozDhw/HH3/8gZCQEAQHB6Nr1666RXCbNm1w+PBhdOjQASUlJbpXc2+8P6BsIdiwYUOj5zT3z6OjoyP69OmDmJgYnDt3Dq1btzbpMWdnV3Zwopv/gQr8bwFaG+U/xykpKRU+p9FokJ6ebtQ/VImqwkNsEdXAgAEDkJ6ervs1taTevXsDgMHj05ZfZso72GsrKioKiqIgMDBQbwELAMeOHav17bu5uaFLly6Ijo6utvNtt90GlUqFkydP1vp+zcXd3R1t27ZFRESEwYW41PeuYcOG6Ny5My5evFjrX93X18fejUyZsXxh+uuvv+LYsWMYMWKE7nMjRoxAcHAwfvvtNwAVd1Mo37WgPpxJrPwQVeW78JjymPP09ARg+B+L5btq1EbPnj0BGH5OOHnypMHFM1FNcRFLVANz5swBADzxxBNIT0+v8PmkpCRcvHjRLPc1bdo0AGW/nr/x14DZ2dm6VzvLt6lL5cc0PXHihN5+sPHx8Vi8eLFZ7mP27NnQaDR47rnncO3aNb3PFRYW6n6127RpUzzyyCM4ceIE3n33XSiKUuG2Tp8+XeEYntKmTZuGkpISLF68WG+mv/76C9u2bYOHh4fZDvt1ozlz5qCgoAAzZ840uNtAdHS0Ucdsra+PvRuZMuMdd9wBBwcHbN68Gbm5uXqL2OHDhyMtLQ2fffYZGjRoUGFf1ueeew4ODg544YUXDB4mKysryyyLv+r88ccfOHbsGBwdHTFw4EDd5TV9zJV/fTcfU3f37t04cuRIred88MEH4e7ujs8//xzh4eG6y0tKSrBkyZJa3z4RwN0JiGrk7rvvxuuvv45Vq1ahffv2uPvuu9G6dWukp6cjIiICx44dw+rVq9G5c+da39eQIUPwwgsv4IMPPkC3bt3w0EMPQVEUfP/994iPj8ecOXP0jt1aV5o1a4aHHnoI33//Pfr164eRI0ciOTkZv/zyC0aOHInIyMha38esWbNw5MgRfPvtt+jQoQMeeOABuLu7IzY2FgcPHsRnn32m+wv5o48+QlhYGBYuXIivvvoKAwcORKNGjRAXF4czZ87g8uXLSExMrPCqsaSFCxdi7969+Oqrr3Dx4kWMHDkSKSkp2LVrF0pLS7Fly5Ya/UraWM888wxOnTqFL774AsePH0dgYCCaN2+O5ORkXLp0CadPn8aOHTsqnFDjZvXhsbdq1SqD+7sCwKJFi0ya0c3NDbfddhtOnjwJOzs7DB06VPe58ldpU1NTMXr06ArHOe7WrRs++ugjzJo1Cx07dsS9996Ldu3aITc3F1FRUThy5AimT5+Ojz/+2GwN1q5dqzvtbGFhIS5fvoz//ve/KC0txZtvvql3KuKaPuYefPBBtGvXDtu2bUNcXBx69+6Nixcv4rfffsO9996Lffv21Wp2Dw8PvP/++5g+fTpuu+02PPbYY/Dw8MAvv/wCFxcXnkaZzIKLWKIaWrlyJYYMGYL3338fQUFByMrKgre3N9q0aYPly5dj8uTJZruv999/H71798bmzZvxn//8BwDQtWtXrFy5EjNmzDDb/dTUtm3b4O/vj++//x4ffPABWrVqhfnz5+OVV17B7t27a337KpUKO3fuxKhRo/Dpp5/iyy+/hKIoaNGiBR555BG9d0h7eXnhxIkT+PDDD7Fr1y5s374dWq0WTZs2Rc+ePfH666+jcePGtZ6pJpydnfHbb7/h7bffxq5du/Dee+/B1dUVQ4cOxauvvoo77rhD5H7Lz+x17733YsuWLfjll1+Ql5eHJk2aoEOHDli7di0CAwONui1LP/aqWkRNnz4dnTp1MmnG4cOH4+TJk+jdu7feiUWaN2+OgIAAhIeHGzziAQDMnDkTvXr1wvr163H06FH897//hYeHB1q1aoUXX3zR7K9Olx99ACjbh9Xb2xuBgYGYPXs27rvvPr1ta/qYc3Fxwf/93//hxRdfRFBQEE6dOoXbb78dR48exS+//FLrRSxQ9uqwh4cHVq9ejS+++AIeHh544IEH8M477+h2ByGqDZVi6PdvRERERET1GPeJJSIiIiKrw0UsEREREVkdLmKJiIiIyOpwEUtEREREVoeLWCIiIiKyOlzEEhEREZHV4SKWiIiIiKwOF7FEREREZHW4iCUiIiIiq8NFLBERERFZHS5iiYiIiMjqcBFLRERERFaHi1giIiIisjpcxBIRERGR1eEiloiIiIisDhexRERERGR1uIglIiIiIqvjYOkBLE2r1SIhIQENGzaESqWy9DhEREREtwRFUZCbm4vmzZvDzq7mr6ve8ovYhIQE+Pn5WXoMIiIioltSXFwcWrZsWePr3fKL2IYNGwIAoqOj4eXlZeFpbEteXh7c3NwsPYZNYlsZ7CqDXWWwqwx2lWGoa05ODvz8/HRrsZq65Rex5bsQNGrUCO7u7haexrYkJyejefPmlh7DJrGtDHaVwa4y2FUGu8qoqqupu3OqFEVRajOUtcvJyYGHhweys7O5iDUzjUYDe3t7S49hk9hWBrvKYFcZ7CqDXWUY6lrbNVi9OjrB0aNHMWbMGDRv3hwqlQp79uyp9jrBwcHo06cPnJyc0L59e2zbtk18TjJORESEpUewWWwrg11lsKsMdpXBrjIkutar3Qny8/PRs2dPPPHEExg/fny120dHR+O+++7Ds88+i+3btyMoKAhPPfUUmjVrhtGjR9fovtNzC1Fi52Tq6GRA4xb+SM8rsvQYNoltZbCrDHaVwa7m5+mqRseOHS09hk2S6FpvdydQqVT48ccfMXbs2Eq3eeWVV7B3715cuHBBd9ljjz2GrKwsHDhwwKj7KX8p23/eTihO3JGbiIiodhRApbH0EEZSoEYpAKBYccLZJaOQdjWGC1kBYWFhFbrWdneCevVKbE2dPHkSgYGBepeNHj0a8+bNq/Q6RUVFKCr6379cc3JypMYjIiK6xShQex2FSp1u6UGMYg8t+tqFAwBOJ74IAGjdurUlR7JZEl3r1T6xNZWUlARfX1+9y3x9fZGTk4Nr164ZvM6aNWvg4eGh++AxYomIiMxEpbGaBawhiUlJuHz5MjIyMnDlyhVcu3YNYWFhAMpeSdRqtQgPD0dBQQHi4uKQnp6OtLQ0xMfHIz8/H5cvX4ZGo9G7zrVr13DlyhVkZmYiJSUFCQkJyMnJQWRkJEpKSvS2LS4uRlRUFLKzs5GYmIikpCRkZWUhJiYGRUVFetuWlpYiIiICubm5uHr1KlJTU5GRkYHY2NgKcyuKUunceXl5BucuLCxETEwMMjMzkZycrJs7KiqqwtwlJSWIiopCTk4OEhMTkZycrJu7sLAQYWFhSEpKQlhYGDQaDSIiIpCXl4eEhIRafb+seneCgIAAzJgxA4sXL9Zdtm/fPtx3330oKCiAi4tLhesYeiXWz8+PuxMQERHVlqoUTr4/AwCKUu4DlPr9Ln81SrDE4WsAwOqSaTi55F7YlRTA09PTwpPZnszMzApdb+ndCZo2bYrk5GS9y5KTk+Hu7m5wAQsATk5OcHKq+AauQy8OgSdPdmBWqamp8PHxsfQYNoltZbCrDHaVUR+7FmuK8cH5swCAF3reBbW92sITVaO0CA1+CwYA3DdiJDxd1UhNzbLoSLaquLjY7Ldp1YvYgQMHYt++fXqXHTp0CAMHDqzxbXm5OcHLjUcnMKeiHDt4s6kItpXBrjLYVUZ97FqsUcHJsezVV283JytYxAK4Pq+zmxNgp4JGYy1vSrMuEl3r1T6xeXl5CA0NRWhoKICyQ2iFhoYiNjYWALB48WJMnTpVt/2zzz6LqKgoLFy4EJcuXcJHH32Eb7/9Fi+++KIlxqeb8LR9cthWBrvKYFcZ7CqDXWVIdK1Xi9gzZ86gd+/e6N27NwBg/vz56N27N5YuXQoASExM1C1oAaBNmzbYu3cvDh06hJ49e2LdunX49NNPa3yMWMD0U55R5dLS0iw9gs1iWxnsKoNdZbCrDHaVIdG1Xu1OMGzYMFT1PjNDZ+MaNmwYzp07V+v75inmzI9HfpDDtjLYVQa7ymBXGewqQ6JrvXollmxLVFSUpUewWWwrg11lsKsMdpXBrjIkunIRe109PdKYVeMZT+SwrQx2lcGuMthVBrvKkOjKRex1fDei+ZUfCJnMj21lsKsMdpXBrjLYVYZEVy5iSUzbtm0tPYLNYlsZ7CqDXWWwqwx2lSHRlYtYEhMXF2fpEWwW28pgVxnsKoNdZbCrDImuXMSSmMaNG1t6BJvFtjLYVQa7ymBXGewqQ6IrF7EkJj8/39Ij2Cy2lcGuMthVBrvKYFcZEl25iCUxdnZ8eElhWxnsKoNdZbCrDHaVIdGV3ykS4+RUv87pbUvYVga7ymBXGewqg11lSHTlIvY6nnbW/LKysiw9gs1iWxnsKoNdZbCrDHaVIdG1Xp121pJ42lnza9q0qaVHsFlsK4NdZbCrjJu7KoqCEm1J7W5UUQBNsclXL9GWANrrx10vLSq7vfrMwNfKx6sMia5cxJKYK1eu8MwnQthWBrvKYFcZN3ZVFAVb/9mKuNxaHMZIAZD8N1CUa54B018FVNb3C18+XmVIdLW+R5cQnnbW/PgkIIdtZbCrDHaVcWPXEm1J7RawAKBozLaA9bNzgSOsaDc9zzaAvRoAH69SJLryldjreNpZ8wsLC+OTgRC2lcGuMthVRmVdF/RbAEc7x5rfYGlR2aunABC4XLeoM4WjnaN1vdfEXg1cn5ePVxkSXbmIJTHt27e39Ag2i21lsKsMdpVRWVdHO0eoTVmAKsr/fv2vdgMcbs136fPxKkOiK3cnIDHR0dGWHsFmsa0MdpXBrjLYVQa7ypDoykUsieE7POWwrQx2lcGuMthVBrvKkOjKRSyJyc7OtvQINottZbCrDHaVwa4y2FWGRFcuYkmMWm36mwKoamwrg11lsKsMdpXBrjIkunIRS2J4/mk5bCuDXWWwqwx2lcGuMiS68jt1nVUdCsRK5OfnW3oEm8W2MthVBrvKYFcZ7CpDoisXsdfxtLPm17hxY0uPYLPYVga7ymBXGewqg11lSHTlIpbExMXV8uwxVCm2lcGuMthVBrvKYFcZEl25iCUxAQEBlh7BZrGtDHaVwa4y2FUGu8qQ6MpF7HWlpaWWHsHmhIeHW3oEm8W2MthVBrvKYFcZ7CpDoisXsSSG/5qVw7Yy2FUGu8pgVxnsKoOvxJJV4b9m5bCtDHaVwa4y2FUGu8rgK7FkVfz8/Cw9gs1iWxnsKoNdZbCrDHaVIdGVi1gSk5aWZukRbBbbymBXGewqg11lsKsMia4OZr9FousaNGhg6RFsFtvKuFW7KoqCEm2J2O07OjuiWFNc1QBAVZ8ngxwdtSguygWAsu+fVlP2idKisqY1xe8BgFv3eUCaRFcuYkmMVqu19Ag2i21l3IpdFUXB1n+2Ii5X7tiYhdcK4eziXMkAAJL/Bq4vxsh4mlIN7B0MnKgn/VVAxV+0mupWfB6oCxJd+Si/jqedNb/iYv6rXgrbyrgVu5ZoS0QXsACgKX+F0BBFwwWsiRQDr7b62bnAEbX8+8yzDWCvrt1tWLFb8XmgLkh05Sux1/G0s+bn4eFh6RFsFtvKuNW7Lui3AI52jma/3by8PLi5uRn+ZGlR2SuHABC4/JZePNWUoa6Odo61f1HGXg3cwi/s3OrPA1IkunIRS2KSkpLQvn17S49hk9hWxq3e1dHOEWqBRWRGaga8PLwMf1JR/verb7Ub4OBk9vu3VRlxyfDybmbpMWzOrf48IEWiK3cnIDFt2rSx9Ag2i21lsKsMdpXBrjLYVYZEVy5ir+NpZ80vIiLC0iPYLLaVwa4y2FUGu8pgVxkSXbmIJTEdO3a09Ag2i21lsKsMdpXBrjLYVYZEVy5iSUxYWJilR7BZbCuDXWWwqwx2lcGuMiS6chFLYlq3bm3pEWwW28pgVxnsKoNdZbCrDImuXMSSmKSkJEuPYLPYVga7ymBXGewqg11lSHTlIpbENGrUyNIj2Cy2lcGuMthVBrvKYFcZEl25iCUxRUVFlh7BZrGtDHaVwa4y2FUGu8qQ6MpF7HU87az58fzTcthWBrvKYFcZ7CqDXWVIdOUi9jqedtb8GjRoYOkRbBbbymBXGewqg11lsKsMia5cxJKYtLQ0S49gs9hWBrvKYFcZ7CqDXWVIdOUilsT4+flZegSbxbYy2FUGu8pgVxnsKkOiKxex1/G0s+YXFRVl6RFsFtvKYFcZ7CqDXWWwqwyJrlzEkhieuk8O28pgVxnsKoNdZbCrDImuDma/RaLrwsLC+GQghG1rTlEUlGhLqtwmPDwcAQEBkkMAmmK52zdBibYE0GrK/lBaVDajmVXZtZ71sCZ8HpDBrjIkunIRS2Latm1r6RFsFtvWjKIo2PrPVsTlxlW5nVarhV2I0C+oFADJfwNFuTK3bw7prwIq83/97bRaIJK/+DM3Pg/IYFcZEl35rEJi4uKqXjCQ6di2Zkq0JdUuYAEgPy9fbghFU68XsH52LnCEzPGyjerq2QawV4vcv63i84AMdpUh0ZWvxJKYxo0bW3oEm8W2plvQbwEc7RwNfi4nJwfu7u4yd1xaVPZKJwAELq93CzZHO0e5k77k5ADVdbVXAzzpTI3weUAGu8qQ6MpFLInJy8uTWxDc4tjWdI52jlBXsoAsvlYMtafQ4lJR/vererUb4OAkcz/1UF5hCdy9bp2vt67weUAGu8qQ6MrdCa7jaWfNj2dBk8O2MthVBrvKYFcZ7CpDoisXsdfxQWt+anX9+nWpLWFbGewqg11lsKsMdpUh0ZWLWBKTnZ1t6RFsFtvKYFcZ7CqDXWWwqwyJrlzEkpimTZtaegSbxbYy2FUGu8pgVxnsKkOiKxex1/G0s+Z35coVS49gs9hWBrvKYFcZ7CqDXWVIdOUilsTwjCdy2FYGu8pgVxnsKoNdZUh05SKWxISFhVl6BJvFtjLYVQa7ymBXGewqQ6IrF7Ekpn379pYewWaxrQx2lcGuMthVBrvKkOjKRSyJiYqKsvQINottZbCrDHaVwa4y2FWGRFcuYklMs2bNLD2CzWJbGewqg11lsKsMdpUh0ZWLWBKTlZVl6RFsFtvKYFcZ7CqDXWWwqwyJrlzEkhhnZ2dLj2Cz2FYGu8pgVxnsKoNdZUh0rXeL2E2bNsHf3x/Ozs4YMGAAQkJCqtx+w4YN6NixI1xcXODn54cXX3wRhYWFNb5fBwcHU0emSqhUKkuPYLPYVga7ymBXGewqg11lSHStV4vYXbt2Yf78+Vi2bBn+/PNP9OzZE6NHj0ZKSorB7Xfs2IFFixZh2bJluHjxIj777DPs2rULr776ah1PToYUFBRYegSbxbYy2FUGu8pgVxnsKkOia716+XH9+vWYOXMmZsyYAQD4+OOPsXfvXnz++edYtGhRhe1PnDiBwYMHY9KkSQAAf39/TJw4EadPn67Tuckwb29vS49gs9jWeIqioERbUtUGgKYYAODt4QaUFskMcv0+bkV8vMpgVxnsKkOia71ZxBYXF+Ps2bNYvHix7jI7OzsEBgbi5MmTBq8zaNAgfP311wgJCUH//v0RFRWFffv2YcqUKZXeT1FREYqK/veXVE5ODgCedlZCfHw8AgICLD2GTWJb4yiKgq3/bEVcblxlGwDHNwKZ0QCA4uxsuHp41OGEtwY+XmWwqwx2lSHRtd7sTpCWlgaNRgNfX1+9y319fZGUlGTwOpMmTcLKlStxxx13wNHREe3atcOwYcOq3J1gzZo18PDw0H34+fkBAPLz83H58mVoNBrdWSXCwsJw7do1XLlyBZmZmUhJSUFCQgJycnIQGRmJkpISvW2Li4sRFRWF7OxsJCYmIikpCVlZWYiJiUFRUZHetqWlpYiIiEBubi6uXr2K1NRUZGRkIDY2FteuXdPbVlEUhIeHo6CgAHFxcUhPT0daWhri4+ORl5dncO7CwkLExMQgMzMTycnJurmjoqIqzF1SUoKoqCjk5OQgMTERycnJurkLCwv1ttVoNIiIiEBeXp5u7vT0dMTGxqKgoEA3b1hYGNq3b4+wsDAUFBQgNjYW6enpSE1NxdWrV5GXl4eIiIhK587KykJycjISExONmjshIQHJycnIzMysdO7Lly8jLy8P8fHxSEtLQ3p6OuLi4lBQUIDw8HDd3Dd+72NjY5GRkaGbOzc3FxERESgtLdXbtqioSDd3UlISEhMTkZ2djaioKBQXF1eYOzIyUjd3SkoKMjMzceXKlQrf+/K58/Pz9eZ2dnbWza3Vag0+ZjMyMpCSkqKbOzIy0uDc0dHRyM7ORlJSEpKSkpCdnY3o6Gij5s7IyDA4t1ar1c0dFxeHtLQ0pKWlIS4uTvezVt3c1f2sVTb3jT9r/1z6B7HZscjJyUFJcQk8tB7ITMv839x52ciKDgUAZGVlw93dHVnZ2SgpLUVeXj4KC4twrbAQefn5KCkpRXZ2NrSKgqysbN11Sks1yM3NQ1FREa5du4b8/AIUF5cgOycHGq1Wb1uNRot0xQPZedduqecILy8vg88R5f/lc4RpzxGKolT6HHHj3HyOqPw5ovxnLTIyUvez5uHhUeXclf2scR1R9XNEhw4dKjxHJCQkoDZUiqIotboFM0lISECLFi1w4sQJDBw4UHf5woULceTIEYO7CAQHB+Oxxx7D6tWrMWDAAERERGDu3LmYOXMmXn/9dYP3Y+iVWD8/P6Snp8PLy8v8X9gtLCwsjOegFsK2xinWFGNNyBoAwIJ+C+Dq4Kr/5oLSImD/wrL/H7Ua4ZEx8q/A2KuBW+yNI3y8ymBXGewqw1DXnJwceHh4IDu77EWEmqo3uxM0btwY9vb2SE5O1rs8OTkZTZs2NXid119/HVOmTMFTTz0FAOjevTvy8/Px9NNP47XXXoOdXcUXmp2cnODk5GT+L4Aq4JOAHLatOUc7x6rfHWuvRkCX7nU30C2Ej1cZ7CqDXWVIdK03uxOo1Wr07dsXQUFBusu0Wi2CgoL0Xpm9UUFBQYWFqr29PYCyX7OQZZX/qoHMj21lsKsMdpXBrjLYVYZE13rzSiwAzJ8/H9OmTUO/fv3Qv39/bNiwAfn5+bqjFUydOhUtWrTAmjVlvx4cM2YM1q9fj969e+t2J3j99dcxZswY3WKWLKdVq1aWHsFmsa0MdpXBrjLYVQa7ypDoWq8WsY8++ihSU1OxdOlSJCUloVevXjhw4IDuzV6xsbF6r7wuWbIEKpUKS5YswdWrV+Hj44MxY8bgjTfesNSXQDdITU3lk4EQtpXBrjLYVQa7ymBXGRJd69UiFgCef/55PP/88wY/FxwcrPdnBwcHLFu2DMuWLauDyaim3NzcLD2CzWJbGewqg11lsKsMdpUh0bXe7BNraTztrPlpNBpLj2Cz2FYGu8pgVxnsKoNdZUh05SKWxBQX37pnKJLGtjLYVQa7ymBXGewqQ6IrF7EkxpRjvpFx2FYGu8pgVxnsKoNdZUh05SL2Ov76wPxuPuYvmQ/bymBXGewqg11lsKsMia5cxF7H48qan7+/v6VHsFlsK4NdZbCrDHaVwa4yJLpyEUtiIiMjLT2CzWJbGewqg11lsKsMdpUh0ZWLWBLDU/fJYVsZ7CqDXWWwqwx2lWHTp50l28NT98lhWxnsKoNdZbCrDHaVIdGVi1gSw/2K5LCtDHaVwa4y2FUGu8rgPrFkVRITEy09gs1iWxnsKoNdZbCrDHaVIdHVpNNUhYaG4uLFi5g4caLusoMHD+KNN95AUVERJk2ahLlz55ptSLJOjRo1svQINkuqraIoKNGWiNw2FAXQ1O1BxEu0JYD2+uHzSovKZrjRTfPwMSuDXWWwqwx2lSHR1aRF7MKFC+Hq6qpbxEZHR2PcuHHw9vZG8+bNMX/+fLi4uODpp58267CSeNpZ8yssLLT0CDZLoq2iKNj6z1bE5caZ/bahAEj+GyjKNf9tGyv9VUBV9S+f+JiVwa4y2FUGu8qQ6GrS7gTnz5/HHXfcofvzl19+CXt7e5w7dw6nT5/Gww8/jI8//thsQ5J14rF35Ui0LdGWyCxgAUDRWHQB62fnAkeoKt/Asw1gr+ZjVgi7ymBXGewqQ6KrSS8/Zmdnw9vbW/fnffv24a677kLjxo0BAHfddRf2799vngnJarm6ulp6BJsl3XZBvwVwtHM03w2WFpW9EgoAgcsBe7X5btsIjnaOUKmqWMTaqwGVio9ZIewqg11lsKsMia4mLWKbNWuGixcvAijbUffs2bOYMWOG7vN5eXmws7Ou94zxtLPml56eDg8PD0uPYZOk2zraOUJtzoWmovzvV/lqN8DByXy3bUZ8zMpgVxnsKoNdZUh0NWkR++CDD+KDDz5AYWEhTp8+DScnJ4wbN073+fPnz6Nt27ZmG7Iu8NcH5teyZUtLj2Cz2FYGu8pgVxnsKoNdZUh0Nenl0tWrV2P8+PH46quvkJKSgm3btsHX1xcAkJOTg927d2PUqFFmHZSsT3R0tKVHsFlsK4NdZbCrDHaVwa4yJLqa9Eqsm5sbtm/fXunn4uPjuU8J8dR9gthWBrvKYFcZ7CqDXWVYxWln7ezs4OHhAUdHM74phKwST90nh21lsKsMdpXBrjLYVYZEV5MPjpqfn4/vv/8eUVFRyMzMrLBPqUqlwsaNG2s9IFkva9sv2pqwrQx2lcGuMthVBrvKkOhq0iI2KCgIEyZMQFZWVqXbcBFLsbGxaNeunaXHsElsK4NdZbCrDHaVwa4yJLqatDvB7Nmz0aBBAxw8eBBZWVnQarUVPnjIKvLx8bH0CDaLbWWwqwx2lcGuMthVhkRXkxaxsbGxWLhwIe666y64u7ubeyaL4GlnzS8vL8/SI9gstpXBrjLYVQa7ymBXGRJdTVrE9ujRA9nZ2eaehWwM/2Egh21lsKsMdpXBrjLYVYZEV5MWsW+//TY++ugjnDlzxtzzkA3hESrksK0MdpXBrjLYVQa7ypDoatKyeOjQodiwYQMGDhyIzp07w8/PD/b29nrbqFQq/PTTT2YZsi5wH17zy8nJgaenp6XHsElsK4NdZbCrDHaVwa4yJLqatIj9/vvv8fjjj0Oj0SA+Ph65ubkVtlGpVLUeri7xtLPm16RJE0uPYLPYVga7ymBXGewqg11lSHQ1aXeCRYsWoWPHjrh06RIyMjIQHR1d4SMqKsrcs5KViY2NtfQINottZbCrDHaVwa4y2FWGRFeTFrEJCQmYNWsWAgICzD0P2RCeuk8O28pgVxnsKoNdZbCrjHpz2tnbbruN/1KhavHUfXKqa6soCoo1xZV/lBahuChX76OkOA/Qaso+SovM+6EprqMytcPHrAx2lcGuMthVhkRXlWLCzqB///03xowZg3feeQePPPKI2YeqSzk5OfDw8EB6ejq8vLwsPY5N0Wg0Fd7wR+ZRVVtFUbD1n62Iy40zfGUFQPLfQFHFfdnLLXYNgFpl0r9xq3fPO4CDk8xt1xIfszLYVQa7ymBXGYa6lq/BsrOzTTrvgEl/S02ePBmlpaWYOHEiPDw80LVrV/To0UPvo2fPnqbcNNkQ7hctp6q2JdqSyhewAKBoqlzA+tm5wBFCb8z0bAPYq2Vu2wz4mJXBrjLYVQa7ypDoatLRCby8vODt7Y0OHTqYex6yIc2bN7f0CDbL2LYL+i2Ao91Nx+YrLQLSXy37/8DlFRaVjnaOckcXsVcD9fjIJXzMymBXGewqg11lSHQ1aREbHBxs5jEsj2foML/MzEw0aNDA0mPYJGPbOto5Qn3zK5+KApTvKqB2q7e/2rcEPmZlsKsMdpXBrjIkugrt9EYEODs7W3oEm8W2MthVBrvKYFcZ7CpDoqvJi9icnBy89dZbGD16NHr37o2QkBAAQEZGBtavX4+IiAizDUnWydpOeGFN2FYGu8pgVxnsKoNdZUh0NWkRGx8fj969e2Pp0qWIj4/HX3/9hby8PABl+8t+8skn+OCDD8w6qDSedtb8CgoKLD2CzWJbGewqg11lsKsMdpUh0dWkHUFffvll5ObmIjQ0FE2aNKlwKrGxY8fil19+McuAdYWnnTU/b29vS49gs9hWBrvKYFcZ7CqDXWVIdDXpldhff/0Vc+bMQZcuXQy+PNy2bVvExVVxiB+6JcTHx1t6BJvFtjLYVQa7ymBXGewqQ6KrSYvYa9euwcfHp9LP5+ZWfgxKunW0b9/e0iPYLLaVwa4y2FUGu8pgVxkSXU1axHbp0gVHjx6t9PN79uxB7969TR6KbMPly5ctPYLNYlsZ7CqDXWWwqwx2lSHR1aRF7Lx587Bz5068/fbbyM7OBgBotVpERERgypQpOHnyJF588UWzDkrWp2PHjpYewWaxrQx2lcGuMthVBrvKkOhq0iL28ccfx8qVK7FkyRIEBAQAAO6++2507NgRO3fuxJtvvomxY8eac06yQmFhYZYewWaxrQx2lcGuMthVBrvKkOhq8mmqXnvtNUyZMgXff/89IiIioNVq0a5dO4wfPx5t27Y154xkpVq1amXpEWwW28pgVxnsKoNdZbCrDImutTrXaqtWrWxmtwF7e3tLj2BzUlJS0Lp1a0uPYZPYVga7ymBXGewqg11lSHTlaWev4xk6zK9hw4aWHsFmsa0MdpXBrjLYVQa7ypDoatQrsXZ2djVe5KlUKpSWlpo0FNkGfv/lsK0MdpXBrjLYVQa7ypDoatQidunSpTb/SiVPO2t+JSUllh7BZrGtDHaVwa4y2FUGu8qQ6GrUInb58uVmv+P6hqedNT93d3dLj2DVFEVBidbAD72iwNlJheIiwycVKdGWANrr/ygrLQJufmxris08qe3gY1YGu8pgVxnsKkOia63e2EVUlZSUFO5bZCJFUbD1n62Iy73p9M0KgOS/UZybDrVaXf0Npb8KqLjru7H4mJXBrjLYVQa7ypDoavLfbqmpqViwYAG6dOkCV1dXuLq6okuXLliwYAGSk5PNOSNZKb6703Ql2pKKC1gAUDRAUS4cHatfwPrZucARVewG5NkGsDdiIXwL4WNWBrvKYFcZ7CpDoqtJr8T+888/GDlyJFJSUjBgwABMmDABABAeHo7169fjq6++QlBQELp162bWYcm6REZG8swnZrCg3wI42jmW/aG0CEh/FVlZ2Wj08HtVLkId7Ryr3pfdXg3Y+L7uNcXHrAx2lcGuMthVhkRXkxaxs2fPhkajwenTp3HbbbfpfS4kJAT33nsvXnjhBRw+fNgsQ5J14pOAeTjaOUJdvlhVFEBlhyaenoDaDXBwsuxwNoaPWRnsKoNdZbCrjHpz2tmQkBDMnTu3wgIWAPr374+5c+fi9OnTtR6OrBtP3ScnKyvb0iPYJD5mZbCrDHaVwa4yJLqatIht0qQJnJ2dK/28s7MzmjRpYvJQZBv8/f0tPYLNatjQzdIj2CQ+ZmWwqwx2lcGuMiS6mrSInTdvHjZv3oykpKQKn0tISMDmzZsxb9682s5Wp3jaWfNLSEiw9Ag2K7+gwNIj2CQ+ZmWwqwx2lcGuMiS6GrVP7Pr16ytc5ubmhvbt22PcuHFo3749AODy5cvYs2cP2rdvb3XHXbX1kzlYgpeXl6VHsFlOTtwXVgIfszLYVQa7ymBXGRJdjVrELliwoNLPbd++vcJlf/31FxYsWIAXX3zR9MnI6l27dg0eHh6WHsMmaUp5hjkJfMzKYFcZ7CqDXWVIdDVqERsdHW3WO62PeNpZIiIiIuth1CL2Vjjwr7Xt/mANXFxcLD2CzbJ34D7cEviYlcGuMthVBrvKkOhaq9PO5ufn48iRI7hy5QqAssXu0KFD0aBBA7MMR9YtIyODv5IRUlRUBO4Va358zMpgVxnsKoNdZUh0NXkR+8EHH2DJkiXIy8vTexWzYcOGeOONN/D888+bZUCyXi1atLD0CDargSv/oSiBj1kZ7CqDXWWwqwyJriYdYuvLL7/E3Llz0a1bN+zYsQOhoaEIDQ3FN998g+7du2Pu3Ln46quvzD0rWZlbYV9qS8nNzbX0CDaJj1kZ7CqDXWWwqwyJrirFhJ1Be/XqhUaNGiEoKKjC8VU1Gg1GjhyJrKwshIaGmmtOMTk5OfDw8EB6ejoPq0H1RrGmGGtC1gAAFvdf/L/TzpYWAfsXlv3/Pe/wtLNERGS1ytdg2dnZcHd3r/H1TXolNiwsDBMmTDB4ggB7e3tMmDCBp20jPgYE8bSzMviYlcGuMthVBrvKqDennfXw8EBMTEyln4+JiTFpRQ0AmzZtgr+/P5ydnTFgwACEhIRUuX1WVhZmz56NZs2awcnJCQEBAdi3b59J903m1bZtW0uPYLMauje09Ag2iY9ZGewqg11lsKsMia4mLWLvu+8+fPDBB9i5c2eFz+3atQsffvghxowZU+Pb3bVrF+bPn49ly5bhzz//RM+ePTF69GikpKQY3L64uBh33XUXYmJisHv3boSFhWHLli0m7TzM086aX2xsrKVHsFl5eXmWHsEm8TErg11lsKsMdpUh0dWkoxO89dZbOHnyJCZPnoyXXnoJHTp0AFB22tmkpCR06tQJb731Vo1vd/369Zg5cyZmzJgBAPj444+xd+9efP7551i0aFGF7T///HNkZGTgxIkTcHR0BAD4+/ub8iXxtLMCfHx8LD2CdVEUKKVFKNGWoERbAmivn4CjtAgo33VdUwwAcHHmcQwl8DErg11lsKsMdpUh0dWkRayPjw/+/PNPfPLJJ9i/f7/uOLHdu3fHK6+8gqeffhrOzs41us3i4mKcPXsWixcv1l1mZ2eHwMBAnDx50uB1fv75ZwwcOBCzZ8/GTz/9BB8fH0yaNAmvvPJKpa+sFhUVoaioSPfnnJycGs1JxsvLyzN5t5JbjqJA+X0DtiYeQZz2mv7n0l8FVPq/NCkpKYG6Dse7VfAxK4NdZbCrDHaVIdG1xrsTXLt2DfPnz8ehQ4cwd+5cHDhwABcvXsTFixdx4MABzJkzp8YLWABIS0uDRqOBr6+v3uW+vr5ISkoyeJ2oqCjs3r0bGo0G+/btw+uvv45169Zh9erVld7PmjVr4OHhofvw8/MDUHbIosuXL0Oj0eh2Pg4LC8O1a9dw5coVZGZmIiUlBQkJCcjJyUFkZCRKSkr0ti0uLkZUVBSys7ORmJiIpKQkZGVlISYmBkVFRXrblpaWIiIiArm5ubh69SpSU1ORkZGB2NhYXLt2TW9bRVEQHh6OgoICxMXFIT09HWlpaYiPj0deXp7BuQsLCxETE4PMzEwkJyfr5o6Kiqowd0lJCaKiopCTk4PExEQkJyfr5i4sLNTbVqPRICIiAnl5ebq509PTERsbi4KCAt28YWFhsLe3R1hYGAoKChAbG4v09HSkpqbi6tWryMvLQ0RERKVzZ2VlITk5GYmJiUbNnZCQgOTkZGRmZlY69+XLl5GXl4f4+HikpaUhPT0dcXFxKCgoQHh4uG7uG7/3sbGxyMjI0M2dm5uLiIgIlJaW6m1bVFSkmzspKQmJiYnIzs5GVFQUiouLK8wdGRmpmzslJQWZacnIiP0LsaUFKC4qe7W1uKgYLe2cUZCdg5KSUuTl5+NaYSEKC4uQ69gYBUWlCA8Ph1arNfiYzcjIQEpKim7uyMhIg3NHR0cjOzsbSUlJSEpKQnZ2NqKjo42aOyMjA1euXKnwmNVqtbh8+TLy8/MRFxeHtLQ0pKWlIS4uDvn5+bh8+XK1c1f3s1bZ3IZ+1iIjI3U/a1XNbW9vX+nPWvncfI6o+XNE+XPAzc8R5f/lc4QRzxGZmRUes0lJSbq58/PzK52bzxE1e47Iy8urcm4+R5j2HOHg4FDhOSIhIQG1YdIhtho0aICNGzfiqaeeqtWd3yghIQEtWrTAiRMnMHDgQN3lCxcuxJEjR3D69OkK1wkICEBhYSGio6N1r7yuX78e7777LhITEw3ej6FXYv38/HiILQEZGRlsaqzSIhTvW4A1BeFAy9uwoO98ONo5wtHO0eCuLhnZefDy9rbAoLaNj1kZ7CqDXWWwqwxDXS1yiK2+ffviwoULply1Uo0bN4a9vT2Sk5P1Lk9OTkbTpk0NXqdZs2YICAjQ23Wgc+fOSEpKQnFxscHrODk5wd3dXe+DZPCA/CZS2cFR7Qa1U0OoHJ3LjgV700cu39glgo9ZGewqg11lsKsMia4mLWI3bNiAnTt34tNPP0VpaalZBlGr1ejbty+CgoJ0l2m1WgQFBem9MnujwYMHIyIiAlqtVndZeHg4mjVrBrWaewxaWpMmTSw9gs1iWxnsKoNdZbCrDHaVIdHVpEXs9OnTYWdnh2eeeQbu7u7o0KEDevTooffRs2fPGt/u/PnzsWXLFnzxxRe4ePEiZs2ahfz8fN3RCqZOnar3xq9Zs2YhIyMDc+fORXh4OPbu3Ys333wTs2fPNuXLIjPjYUrksK0MdpXBrjLYVQa7yqg3h9jy8vKCt7c3OnbsaNZhHn30UaSmpmLp0qVISkpCr169cODAAd2bvWJjY2Fn9791t5+fHw4ePIgXX3wRPXr0QIsWLTB37ly88sorZp2LTGPuxwf9D9vKYFcZ7CqDXWWwqwyJria9scuWlO9UzDd2mV9YWBifDIx14xu7/AZg8e1LoLavfJcYtpXBrjLYVQa7ymBXGYa6WuSNXUTGKD8JBpkf28pgVxnsKoNdZbCrDImuJi9ii4qK8OGHH+Lee+9Fly5d0KVLF9x777348MMPUVhYaM4Z6wRPO2t+kZGRlh7BZrGtDHaVwa4y2FUGu8qQ6GrSIjY+Ph69evXCnDlzcP78efj4+MDHxwfnz5/HnDlz0KtXL8THx5t7VlE87az5NW/e3NIj2Cy2lcGuMthVBrvKYFcZEl1NWsTOnj0bV65cwbfffourV6/iyJEjOHLkCK5evYpdu3YhNjaWRwggZGRkWHoEm8W2MthVBrvKYFcZ7CpDoqtJRycICgrCiy++iIcffrjC5yZMmIA///wTH3zwQa2Hq0s3HmuWzMPFxcXSI9gstpXBrjLYVQa7ymBXGRJdTXoltmHDhlUetLZp06Zo2LChyUNZAhexRERERNbDpEXsjBkzsG3bNhQUFFT4XF5eHrZu3Yonn3yy1sORdbt27ZqlR7BZbCuDXWWwqwx2lcGuMiS6mrQ7Qa9evbB371506tQJ06ZNQ/v27QEAly9fxpdffgkvLy/06NEDP/zwg971xo8fX/uJyWrwuLty2FYGu8pgVxnsKoNdZUh0NWkR+9hjj+n+/4033qjw+fj4eEycOBE3nkdBpVJBo9GYcndkpRISEni8PSFsK4NdZbCrDHaVwa4yJLqatIg9fPiwWYcg29SuXTtLj2Cz2FYGu8pgVxnsKoNdZUh0NWkRO3ToUHPPQTbo8uXLVnXqPkVRUKItkbpxQFNc+ec1xSiB8W8utLa21oJdZbCrDHaVwa4yJLqqlBt/538LKj9vb3p6OveDuYUpioKt/2xFXG6cwI0DSP4bKMo1bnu/AVh8+xKo7dXmn4WIiKieKF+DZWdnw93dvcbXN/m0s7aGp501v7CwMEuPYLQSbYnMAhYAFI3xC1inhvBzbw1HO8cqN7OmttaEXWWwqwx2lcGuMiS6mrQ7gS3iaWfNr1WrVpYewSQL+i2odhFZI6VFQPqrZf8fuByo6hVWezUc7dXVPh6ttW19x64y2FUGu8pgVxkSXflKLIlJSUmx9AgmcbRzhNpebd4PlV3Zh9oNaqeGlX84OBn1DyprbVvfsasMdpXBrjLYVYZEV6MWsT///DMSEhLMfuf1Cc/YZX7WdtY2a8K2MthVBrvKYFcZ7CpDoqtRi9hx48YhODhY9+e2bdvi559/NvswlsRFrPmVlpZaegSbxbYy2FUGu8pgVxnsKkOiq1GL2IYNGyIrK0v355iYGOTl5Zl9GLItfCKQw7Yy2FUGu8pgVxnsKkOiq1Fv7Orfvz/eeOMNJCcnw8PDAwCwb98+JCUlVXodlUqFF1980TxTklVyc3Oz9Ag2i21lsKsMdpXBrjLYVYZEV6MWsR999BGmTp2KVatWAShboO7YsQM7duyo9DpcxFJqaqpJx32j6rGtDHaVwa4y2FUGu8qQ6GrUIrZ9+/Y4ceIECgsLkZKSAn9/f2zYsAEPPvigWYch28LDlMhhWxnsKoNdZbCrDHaVYfFDbDk7O6NVq1ZYtmwZRowYgdatW1f5Qbe2qKgoS49gs9hWBrvKYFcZ7CqDXWVIdDXpZAfLli3T/X9eXh7i4srOdOTn58d9SUiH556Ww7Yy2FUGu8pgVxnsKkOiq8knO/jjjz8wfPhweHp6olu3bujWrRs8PT0xYsQInDlzxpwz1gmedtb8eOo+OWwrg11lsKsMdpXBrjLqzWlnT58+jWHDhkGtVuOpp55C586dAQAXL17EN998gyFDhiA4OBj9+/c367CSeNpZ82vTpo2lR7BZbCuDXWWwqwx2lcGuMiS6mvRK7GuvvYYWLVogLCwMmzdvxpw5czBnzhxs3rwZYWFhaN68OV577TVzz0pW5urVq5YewWaxrQx2lcGuMthVBrvKkOhq0iL29OnTeOaZZ9C0adMKn/P19cXTTz+NU6dO1Xq4usQzdpmfl5eXpUewWWwrg11lsKsMdpXBrjIkupq0iLWzs6vyzAsajQZ2dibvbmsRXMSa37Vr1yw9gs1iWxnsKoNdZbCrDHaVIdHVpJXmoEGDsGnTJly5cqXC52JjY/HRRx9h8ODBtR6OiIiIiMgQk97Y9eabb2LIkCHo1KkTxo0bh4CAAABl7zz76aef4ODggDVr1ph1ULI+Li4ulh6hSoqioERbAgC6/5pwI4CmuOptqvu8Cep7W2vFrjLYVQa7ymBXGRJdTVrE9u7dG6dPn8Zrr72Gn3/+GQUFBQAAV1dX3H333Vi9ejW6dOli1kHJ+mRkZMDDw8PSYxikKAq2/rMVcblxtbkR4PhGIDPafIMZqT63tWbsKoNdZbCrDHaVIdHVpEUsAHTp0gU//vgjtFotUlNTAQA+Pj5Wty8syWnevLmlR6hUibbE4ALWr6EfHO0cjbsRTXHNFrCebQB7tfHbV6E+t7Vm7CqDXWWwqwx2lSHR1eRFbDk7Ozv4+vqaYxayMTExMVZx5pMF/RboFq6Odo6mHTN41OrqF6j2asBMxyO2lrbWhl1lsKsMdpXBrjIkutZ6EUtUGWt5EnC0c4S6tq+Q2qsBByfzDGQEa2lrbdhVBrvKYFcZ7CqjXp121tZwNwjz46n75LCtDHaVwa4y2FUGu8qQ6MqV23VcxJpfu3btLD2CzWJbGewqg11lsKsMdpUh0ZUrNxJj6DjCZB5sK4NdZbCrDHaVwa4yJLpyEXsdz9hlfk2aNLH0CDaLbWWwqwx2lcGuMthVhkTXWr2x699//0VUVBQyMzOhKEqFz0+dOrU2N1+nuIg1v5ycHDRs2NDSY9gktpXBrjLYVQa7ymBXGRJdTVrERkZG4vHHH0dISIjBxSsAqFQqq1rEkvk5Ohp5vFWqMbaVwa4y2FUGu8pgVxkSXU1axD7zzDP4+++/sWHDBtx5553w9PQ091xkAxwceAQ3KWwrg11lsKsMdpXBrjIkupp0i8ePH8err76KF154wdzzkA3Jzc2Fl5eXpcewSWwrg11lsKsMdpXBrjIkupr0xq7GjRvzvMJULe4cL4dtZbCrDHaVwa4y2FWGRFeTFrHPPvssvv76a2g0GnPPQzYkNjbW0iPYLLaVwa4y2FUGu8pgVxkSXU3anSAgIAAajQY9e/bEE088AT8/P9jb21fYbvz48bUekKwXT90nh21lsKsMdpXBrjLYVYZEV5MWsY8++qju/xcsWGBwG5VKZVWv1PKMXeYXFhbGJwMhbCuDXWWwqwx2lcGuMiS6mrSIPXz4sFmHqA+4iDW/Dh06WHoEm8W2MthVBrvKYFcZ7CpDoqtJi9ihQ4eaew6yQREREQgICLD0GDaJbWWwqwx2lcGuMthVhkTXWh+0699//9WdD7d169bo0qVLrYeyBJ6xy/xatmxp6REMUhQFJdqSmlwB0BRXvNzQZXWkvra1duwqg11lsKsMdpUh0dXkRexPP/2E+fPnIyYmRu/yNm3aYP369XjggQdqO1ud4iLW/NLT0+Hq6mrpMfQoioKt/2xFXG6csVcAjm8EMqNlB6uh+tjWFrCrDHaVwa4y2FWGRFeTdgTdt28fHnroIQDAm2++iR9//BE//vgj3nzzTSiKgvHjx+PAgQNmHZSsT318EijRlugtYP0a+sHRropT4WmKq1/AerYB7NVmmtA49bGtLWBXGewqg11lsKsMia4mvRK7atUq9OjRA8eOHUODBg10lz/wwAN4/vnncccdd2DFihW4++67zTYoWR9FUSw9QpUW9FsAVwdXqFQq464warXhxaq9GjD2Nsykvre1Vuwqg11lsKsMdpUh0dWkV2L/+usvTJs2TW8BW65BgwaYPn06/vrrr1oPR9atsLDQ0iNUydHO0fgFLFC2WHVwqvhRxwtYoP63tVbsKoNdZbCrDHaVIdHVpEWss7MzMjIyKv18RkYGnJ2dTR6KbIOnp6elR7BZbCuDXWWwqwx2lcGuMiS6mrSIHTFiBDZu3IiTJ09W+Nzp06fx/vvvIzAwsNbDkXVLSEiw9Ag2i21lsKsMdpXBrjLYVYZEV5P2iX3nnXcwcOBA3HHHHejfv7/uDAxhYWEICQlBkyZN8Pbbb5t1ULI+bdu2tfQINottZbCrDHaVwa4y2FWGRFeTXolt06YN/vrrL8yZMweZmZnYtWsXdu3ahczMTMydOxfnz5+Hv7+/mUeVxTN2mV9ERISlR7BZbCuDXWWwqwx2lcGuMiS6qpRb/G14OTk58PDwQHZ2Ntzd3S09Dgkr1hRjTcgaAMDi/ouhru7QWKVFwP6FZf9/zztlb+QiIiKiWqvtGowvP5KYsLAwS49gs9hWBrvKYFcZ7CqDXWVIdDVqn9gnnngCKpUK//nPf2Bvb48nnnii2uuoVCp89tlntR6wrvCMXebXqlUrS49gs9hWBrvKYFcZ7CqDXWVIdDVqEfvbb7/Bzs4OWq0W9vb2+O2336o9vmaNjr9ZD3ARa34pKSlo3bq1pcewSWwrg11lsKsMdpXBrjIkuhq1iI2Jianyz0SGcB9jOWwrg11lsKsMdpXBrjIkupq0T2xsbCyuXbtW6eevXbuG2NhYk4ci21BSUmLpEWwW28pgVxnsKoNdZbCrDImuJh9i68cff6z08z///DPatGlj8lBkG0pLSy09gs1iWxnsKoNdZbCrDHaVIdHVpEVsdUflKikp4XFXCW5ubpYewWaxrQx2lcGuMthVBrvKkOhq9EozJycHsbGxut0E0tPTdX++8eOvv/7Czp070axZM5OH2rRpE/z9/eHs7IwBAwYgJCTEqOvt3LkTKpUKY8eONfm+yXxSU1MtPYLNYlsZ7CqDXWWwqwx2lSHR1ejTzr733ntYuXIlgLIjD8ybNw/z5s0zuK2iKFi9erVJA+3atQvz58/Hxx9/jAEDBmDDhg0YPXo0wsLC0KRJk0qvFxMTgwULFuDOO+806X7J/HiYEjlsK4NdZbCrDHaVwa4yLHaILQAYNWoU3NzcoCgKFi5ciIkTJ6JPnz5626hUKjRo0AB9+/ZFv379TBpo/fr1mDlzJmbMmAEA+Pjjj7F37158/vnnWLRokcHraDQaTJ48GStWrMCxY8eQlZVV4/vl7g/mFxUVhY4dO1p6DCiKghJt2Q7l5f814kqAprjsox6qL21tDbvKYFcZ7CqDXWVIdDV6ETtw4EAMHDgQAJCfn4+HHnoI3bp1M+swxcXFOHv2LBYvXqy7zM7ODoGBgTh58mSl11u5ciWaNGmCJ598EseOHavyPoqKilBUVKT7c05Oju5+yLzqw5OAoijY+s9WxOXG1eRKwPGNQGa03GC1VB/a2iJ2lcGuMthVBrvKkOha45VbQUEB3n//fezfv9/sw6SlpUGj0cDX11fvcl9fXyQlJRm8zu+//47PPvsMW7ZsMeo+1qxZAw8PD92Hn58fgLKF+eXLl6HRaHSnRgsLC8O1a9dw5coVZGZmIiUlBQkJCcjJyUFkZCRKSkr0ti0uLkZUVBSys7ORmJiIpKQkZGVlISYmBkVFRXrblpaWIiIiArm5ubh69SpSU1ORkZGhO3zZjdsqioLw8HAUFBQgLi4O6enpSEtLQ3x8PPLy8gzOXVhYiJiYGGRmZiI5OVk3d1RUVIW5S0pKEBUVhZycHCQmJiI5OVk3d2Fhod62Go0GERERyMvL081dvn90QUGBbt6wsDBcunQJYWFhKCgoQGxsLNLT05GamoqrV68iLy8PERERlc6dlZWF5ORkJCYmGjV3QkICkpOTkZmZqTd3ibYEF+IuQNEqyM7ORklJCfLz8uFl54WczBzExcWhoKAA4eHhurmhKUZWdChKSzXIzctDYVER8tRNcDUpFbm5uYiIiEBpaaneLEVFRbq5k5KSkJiYiOzsbERFRaG4uLjC3JGRkbq5U1JSkJmZiStXrlT43ms0Gly+fBn5+fmIj49HWloa0tPTERISoptbq9UafMxmZGQgJSUFV69eRW5uLiIjIw3OHR0djezsbCQlJSEpKQnZ2dmIjo42au6MjAyDc2u1Wt3ccXFxSEtLQ1paGuLi4nQ/a9XNXd3PWmVzG/pZi4yM1P2sVTX3pUuXKv1Z43OE6c8R586dM/gcUf5fSz5H3PyzlpeXp/ezZvA54obvfWxsLDIyMnRz1+VzxPHjxyt9jrhxbj5H1Ow54s8//6xybj5HmPYcUf5x43NEQkICakOlVHeoAQN8fX2xbNkyPPfcc7W685slJCSgRYsWOHHihO5VXwBYuHAhjhw5gtOnT+ttn5ubix49euCjjz7CPffcAwCYPn06srKysGfPHoP3YeiVWD8/P2RlZcHDw8OsX8+trri4GGq12rIzaIqxJmQNAGBBvwVwtHMEADjaOVZ+VrnSImD/wrL/H7UasFeXfdSjs9DVh7a2iF1lsKsMdpXBrjIMdc3JyYGHhweys7NNOhmCSb9Df+ihh7B79+5qD7VVU40bN4a9vT2Sk5P1Lk9OTkbTpk0rbB8ZGYmYmBiMGTMGDg4OcHBwwJdffomff/4ZDg4OiIyMrHAdJycnuLu7630AZfvVknnFx8dbegQ9jnaOUNurobZXG39aZHs14OBUrxawQP1rayvYVQa7ymBXGewqQ6Kr0fvE3uixxx7Dc889h+HDh2PmzJnw9/eHi4tLhe1ufuNXddRqNfr27YugoCDdYbK0Wi2CgoLw/PPPV9i+U6dO+Pvvv/UuW7JkCXJzc7Fx40bdrgJkGd7e3pYewWaxrQx2lcGuMthVBrvKkOhq0iJ22LBhuv839EYqRVGgUqlMenVz/vz5mDZtGvr164f+/ftjw4YNyM/P1x2tYOrUqWjRogXWrFkDZ2fnCm8ua9SoEQCY/U1nVHMFBQXcRUMI28pgVxnsKoNdZbCrDImuJi1it27datYhbvToo48iNTUVS5cuRVJSEnr16oUDBw7o3uwVGxvLIwlYCaN/ZU81xrYy2FUGu8pgVxnsKkOiq0mL2GnTppl7Dj3PP/+8wd0HACA4OLjK627bts38A5FJnJ2dLT2CzWJbGewqg11lsKsMdpUh0bXWL2nm5eXh4sWLuHjxIvLy8swxE9kIU046QcZhWxnsKoNdZbCrDHaVIdHV5EXsH3/8geHDh8PT0xPdunVDt27d4OnpiREjRuDMmTPmnJGsVLNmzSw9gs1iWxnsKoNdZbCrDHaVIdHVpEXs6dOnMWTIEPz555946qmn8N577+G9997DU089hT///BNDhgxBSEiIuWcVxf1szS8mJsbSI9gstpXBrjLYVQa7ymBXGRJdTTrZQWBgIGJiYvD7779XOH5rcnIyBg8ejDZt2uDQoUNmG1RKbQ+0S/XbjSc7WNx/MdT2RhzA+saTHdzzTtlxYomIiMisLHKyg9OnT+OZZ54xeAICX19fPP300zh16pQpN002pPyUdGR+bCuDXWWwqwx2lcGuMiS6mrSItbOzQ2lpaaWf12g0VvfreXOffYyAdu3aWXoEm8W2MthVBrvKYFcZ7CpDoqtJK81BgwZh06ZNuHLlSoXPxcbG4qOPPsLgwYNrPVxd4mlnzY/7FclhWxnsKoNdZbCrDHaVIdHVpOPEvvnmmxgyZAg6deqEcePGISAgAEDZS8U//fQTHBwcsGbNGrMOStan/AQVZH5sK4NdZbCrDHaVwa4yJLqatIjt3bs3Tp8+jddeew0///wzCgoKAACurq64++67sXr1anTp0sWsg5L1ycnJQcOGDS09hk1iWxnsKoNdZbCrDHaVIdHVpEUsAHTp0gU//vgjtFotUlNTAQA+Pj5Wty8syVGrjTgSAJmEbWWwqwx2lcGuMthVhkRXkxex5VQqle58uDzfMN3I3t7e0iPYLLaVwa4y2FUGu8pgVxkSXU1+2fTff//Fww8/DHd3dzRr1gzNmjWDu7s7Hn74YVy4cMGcM5KVsvRpiBVFwdYLWy06gxRLt7VV7CqDXWWwqwx2lSHR1aRXYo8dO4Z77rkHWq0WDz74oN4bu37++Wfs378fBw4cwJ133mnWYcm6+Pj4WPT+S7QlSCpIAgA0dW0KRztHi85jTpZua6vYVQa7ymBXGewqQ6KrSYvYF198EU2aNMGRI0fg5+en97m4uDgMGTIE8+fPxx9//GGWIesC9+U1v9jYWHTs2NHSYwAAZnSbYVO7u9SntraEXWWwqwx2lcGuMiS6mrRy++eff/Dcc89VWMACgJ+fH2bNmoV//vmn1sPVJS5izY9PAnLYVga7ymBXGewqg11lSHQ1aeXWunVrFBUVVfr54uJigwtcurXw1H1y2FYGu8pgVxnsKoNdZdSb084uXboU77//PkJDQyt87ty5c/jggw+wfPnyWo5Wt3jaWfMr31eazI9tZbCrDHaVwa4y2FWGRFeT9ok9deoUfH190bdvXwwaNAjt27cHAFy+fBknT55Et27dcPLkSZw8eVJ3HZVKhY0bN5pnagE87az5Xb58mU8GQthWBrvKYFcZ7CqDXWVIdFUpJrwEacr+oyqVql4uFHNycuDh4YH09HR4eXlZehybUlBQAFdXV4vdf7GmGGtCyk5/vLj/YqjtjTzQcmkRsH9h2f/f8w7g4CQ0oeks3dZWsasMdpXBrjLYVYahruVrsOzsbLi7u9f4Nk3anUCr1db4oz4uYElWenq6pUewWWwrg11lsKsMdpXBrjIkuvIt+SSG/5KVw7Yy2FUGu8pgVxnsKkOia61OOxsdHY39+/fjypUrAMqOWnDPPfegTZs2ZhmOrBvfLCeHbWWwqwx2lcGuMthVhkRXkxexL730EjZu3AitVqt3uZ2dHebNm4e1a9fWejiyboWFhZYewWaxrQx2lcGuMthVBrvKkOhq0u4E69atw3vvvYfx48fj5MmTyMrKQlZWFk6ePImHH34Y7733Ht577z1zz0pWplGjRpYewWaxrQx2lcGuMthVBrvKkOhq0iJ2y5YteOCBB/Dtt99iwIABcHd3h7u7OwYMGICdO3dizJgx+OSTT8w9qyiescv8EhMTLT2CzWJbGewqg11lsKsMdpUh0dWklVtMTAxGjx5d6edHjx6NmJgYU2eyCC5iza9t27aWHsFmsa0MdpXBrjLYVQa7ypDoatLKrUmTJjh//nylnz9//jx8fHxMHopsQ0REhKVHsFlsK4NdZbCrDHaVwa4yJLqatIidMGECPv30U7z11lvIz8/XXZ6fn4+3334bn376KR599FGzDVkX+G5E8+vYsaOlR7BZbCuDXWWwqwx2lcGuMiS6mrSIXbVqFYYOHYpXX30Vnp6e8Pf3h7+/Pzw9PbF48WIMHToUK1euNPesongyBvMLCwuz9Ag2i21lsKsMdpXBrjLYVYZEV5MOseXq6oqgoCD89NNPeseJvfvuu3HvvfdizJgxUKlUZh2UrE/r1q0tPYLNYlsZ7CqDXWWwqwx2lSHRtcaL2IKCAjz++ON46KGHMHnyZDz44INmH4psQ1JSEvz9/S09hk1iWxnsKoNdZbCrDHaVIdG1xrsTuLq64v/+7/9QUFBg1kHI9nh4eFjsvhVFwdYLW025InD8ffMPZGaWbGvL2FUGu8pgVxnsKkOiq0n7xN5xxx04efKkuWchG1NcXGyx+y7RliCpIAkA0NS1KRztHI27oqYYyIkv+3/3loC9WmjC2rFkW1vGrjLYVQa7ymBXGRJdTVrEfvjhhzh27BiWLFmC+Ph4c89ENqK+vFluRrcZpu2jPXgOUE/37a4vbW0Nu8pgVxnsKoNdZUh0NWkR27NnT8THx2PNmjVo3bo1nJycdGftKv/gy/Hk5uZm6RFsFtvKYFcZ7CqDXWWwqwyJriYdneChhx6yuaMP2NrXUx+kpaXB3d3d0mPYJLaVwa4y2FUGu8pgVxkSXU1axG7bts2sQ9QH9vb2lh7B5vj5+Vl6BJvFtjLYVQa7ymBXGewqQ6JrjXYnKCwsxK5du/DWW2/h008/RWJiotkHItsRFRVl6RFsFtvKYFcZ7CqDXWWwqwyJrka/EpuSkoJBgwYhOjpad4pWV1dX7NmzB4GBgWYfrK7xtLPmx1P3yWFbGewqg11lsKsMdpVh0dPOrlq1CjExMXjxxRfxyy+/YMOGDXBxccEzzzxj9qEsge9GND+euk8O28pgVxnsKoNdZbCrDIuedvbXX3/F1KlTsXbtWt1lvr6+mDRpEsLCwvgvF6qgbdu2lh7BZrGtDHaVwa4y2FUGu8qQ6Gr0K7GxsbG444479C674447oCgKkpOTzT4YWb+4uDhLj2Cz2FYGu8pgVxnsKoNdZUh0NXoRW1RUBGdnZ73Lyv9cWlpq3qnIJjRu3NjSI9gstpXBrjLYVQa7ymBXGRJda3SIrZiYGPz555+6P2dnZwMALl++jEaNGlXYvk+fPrWbjqxafn4+j7UnhG1lsKsMdpXBrjLYVYZE1xotYl9//XW8/vrrFS5/7rnn9P6sKApUKhXfLHWLs7Mz6YRwZAS2lcGuMthVBrvKYFcZEl2NXsRu3brV7HdOts3JycnSI9gstpXBrjLYVQa7ymBXGRJdjV7ETps2zex3Xp/wtLPml5WVZXA3E6o9tpXBrjLYVQa7ymBXGRJd+Zr5dTztrPk1bdrU0iPYLLaVwa4y2FUGu8pgVxkSXbmIJTFXrlyx9Ag2i21lsKsMdpXBrjLYVYZE1xq9scuW8bSz5lfXJ8BQFAUl2hIA0P23hjcAaIrNPJUMnlxEBrvKYFcZ7CqDXWVIdOUi9joeScH86vJMboqiYOs/WxGXa+LBlBUFOL4RyIw272BCeJY8Gewqg11lsKsMdpUh0ZW7E5CY9u3b19l9lWhLDC5g/Rr6wdHOsfob0BTrL2A92wD2ajNOaF512fZWwq4y2FUGu8pgVxkSXflKLImJjo62yJPBgn4LdAtXRzvHmh95YtRqQO0G1OMjVliqra1jVxnsKoNdZbCrDImuXMSSGEu9w9PRzhHq2ryKaq+u1wtYgO+elcKuMthVBrvKYFcZPDoBWZXy0xKT+bGtDHaVwa4y2FUGu8qQ6MpFLIlRq+vvPqXWjm1lsKsMdpXBrjLYVYZEVy5iSQzPPy2HbWWwqwx2lcGuMthVhkRXfqeu42lnzS8/P9/SI9gstpXBrjLYVQa7ymBXGRJduYi9jqedNb/GjRtbegSbxbYy2FUGu8pgVxnsKkOiKxexJCYuzsQTD1C12FYGu8pgVxnsKoNdZUh05SKWxAQEBFh6BJvFtjLYVQa7ymBXGewqQ6IrF7HXlZaWWnoEmxMeHm7pEWwW28pgVxnsKoNdZbCrDImuXMSSGP5rVg7bymBXGewqg11lsKsMvhJLVoX/mpXDtjLYVQa7ymBXGewqg6/EklXx8/Oz9Ag2i21lsKsMdpXBrjLYVYZE13q5iN20aRP8/f3h7OyMAQMGICQkpNJtt2zZgjvvvBOenp7w9PREYGBgldtT3UlLS7P0CDaLbWWwqwx2lcGuMthVhkTXereI3bVrF+bPn49ly5bhzz//RM+ePTF69GikpKQY3D44OBgTJ07E4cOHcfLkSfj5+WHUqFG4evVqHU9ON2vQoIGlR7BZbCuDXWWwqwx2lcGuMiS61rtF7Pr16zFz5kzMmDEDXbp0wccffwxXV1d8/vnnBrffvn07nnvuOfTq1QudOnXCp59+Cq1Wi6CgoDqenG6m1WotPYLNYlsZ7CqDXWWwqwx2lSHRtV4tYouLi3H27FkEBgbqLrOzs0NgYCBOnjxp1G0UFBSgpKQEXl5eNbpvnnbW/IqLiy09QtUUBSgtKvvQ1PNZb1Lv21opdpXBrjLYVQa7ypDo6mD2W6yFtLQ0aDQa+Pr66l3u6+uLS5cuGXUbr7zyCpo3b663EL5RUVERioqKdH/OyckBwNPOSvDw8LD0CJVTFOD4RiAz2tKTmKRet7Vi7CqDXWWwqwx2lSHRtV69Eltbb731Fnbu3Ikff/wRzs7OBrdZs2YNPDw8dB/l75bLz8/H5cuXodFoEBYWBgAICwvDtWvXcOXKFWRmZiIlJQUJCQnIyclBZGQkSkpK9LYtLi5GVFQUsrOzkZiYiKSkJGRlZSEmJgZFRUV625aWliIiIgK5ubm4evUqUlNTkZGRgdjYWFy7dk1vW0VREB4ejoKCAsTFxSE9PR1paWmIj49HXl6ewbkLCwsRExODzMxMJCcn6+aOioqqMHdJSQmioqKQk5ODxMREJCcn6+YuLCzU21aj0SAiIgJ5eXm6udPT0xEbG4uCggLdvGFhYUhMTERYWBgKCgoQGxuL9PR0pKam4urVq8jLy0NERESlc2dlZSE5ORmJiYlGzZ2QmIBrBddQVFSEK1euGJz78uXLyMvLQ3x8PNJSElCYeAl5efkoKS1FVnY2FAVILXUD7NW6731sbCwyMjJ0c+fm5iIiIgKlpaV6t19UVKSbOykpCYmJicjOzkZUVBSKi4srzB0ZGVk2d0ICUlJSkJmZiStXrlT43pfPnZ+fXzZ3WhrS09Nx6dIlFBQUIDw8HFqt1uBjNiMjAykpKbq5IyMjDc4dHR2N7OxsJCUlISkpCdnZ2YiOjjZq7oyMDINza7Va3dxxcXFIS0tDWloa4uLidD9r1c1d3c9aZXMb+lmLjIzU/axVNXdiYmKlP2t8jjD9OSIqKsrgc0T5f+vkOSIhAcnJycjMzKx0br3niOs/a3FxcbqftfJ5b/zeW/I54u+//670OeLGufkcUbPniIiIiCrn5nOEac8RSUlJFZ4jEhISUBsqRVGUWt2CGRUXF8PV1RW7d+/G2LFjdZdPmzYNWVlZ+Omnnyq97tq1a7F69Wr83//9H/r161fpdoZeifXz80N2djbc3d3N8nVQGY1GU2evcBdrirEmZA0AYHH/xVDbq6u+QmkRsH9h2f+PWg2Ub2+vBqxg15K6bHsrYVcZ7CqDXWWwqwxDXXNycuDh4WHyGqxevRKrVqvRt29fvTdllb9Ja+DAgZVe75133sGqVatw4MCBKhewAODk5AR3d3e9D4CnnZUQERFh6RGMY68GHJzKPqxgAQtYUVsrw64y2FUGu8pgVxkSXevVPrEAMH/+fEybNg39+vVD//79sWHDBuTn52PGjBkAgKlTp6JFixZYs6bsVbe3334bS5cuxY4dO+Dv74+kpCQAgJubG9zc3Cz2dRDQsWNHS49gs9hWBrvKYFcZ7CqDXWVIdK1Xr8QCwKOPPoq1a9di6dKl6NWrF0JDQ3HgwAHdm71iY2ORmJio237z5s0oLi7Gww8/jGbNmuk+1q5da6kvga4r31+GzI9tZbCrDHaVwa4y2FWGRNd690osADz//PN4/vnnDX4uODhY788xMTHyA5FJWrdubekRbBbbymBXGewqg11lsKsMia717pVYsh3lu3aQ+bGtDHaVwa4y2FUGu8qQ6MpFLIlp1KiRpUewWWwrg11lsKv5xMTEQKVSITQ0tF50DQ4OhkqlQlZWlsVmuLFJZWoyZ3Vdhw0bhnnz5tVoRlOoVCrs2bNH/H7qisTjtV7uTkC24cZDmZF5sa0MdpXBrjLqQ9dBgwYhMTHRpk4QUF3XH374AY6OjuJzJCYmwtPTE0DZQr1NmzY4d+4cevXqJX7fEiQer1zEXsfTzpofzz8th21lsKsMdpVRH7qq1Wo0bdrU0mOYVXVdqzutfXFxMdTqao5VboRbraspuDvBdTywsfk1aNDA0iPYLLaVwa4y6qKrVqsgPa/Ioh9arfHnDtq9eze6d+8OFxcXeHt7IzAwEPn5+dBqtVi5ciVatmwJJycn9OrVCwcOHDB4Gy4uLmjZsiU2b96sd/m5c+dgZ2eHK1euAACysrLw1FNPwcfHB+7u7hgxYgTOnz9f7Yzh4eFQqVQVTvv+3nvvoV27dgAq/po+PT0dEydORIsWLeDq6oru3bvjm2++MbrLgQMHcMcdd6BRo0bw9vbG/fffj8jISL1tQkJC0Lt3bzg7O6Nfv344d+5chdvZt28fAgIC4OLiguHDh9foTeANGjTA8ePHMWzYMLi6usLT0xOjR49GZmYmgIq7E/j7+2PVqlWYOnUq3N3d8fTTTwNAlbfh7++PDRs26N1vr169sHz5ct2fb9ydoE2bNgCA3r17Q6VSYdiwYUZ/PfWFxPMAX4klMWlpaTwLmhC2lcGuMuqia2ZBMfqu/j/R+6jO2SWB8HZzqna7xMRETJw4Ee+88w7GjRuH3NxcHDt2DIqiYOPGjVi3bh0++eQT9O7dG59//jkeeOAB/PPPP+jQoYPe7WRkZGDixInYsWMHZs2apbt8+/btGDx4sO7d4BMmTICLiwv2798PDw8PfPLJJxg5ciTCw8OrfFUxICAA/fr1w/bt27Fq1Sq92580aZLB6xQWFqJv37545ZVX4O7ujr1792LKlClo164d+vfvX22b/Px8zJ8/Hz169EBeXh6WLl2KcePGITQ0FHZ2dsjLy8P999+Pu+66C19//TWio6Mxd+5cvduIi4vD+PHjMXv2bDz99NM4c+YMXnrppWrvu9zRo0fx8MMP44knnsDGjRvh4OCAw4cPQ6PRVHqd8kODLlu2DAAQGhqKkSNH1ug2qhISEoL+/fvj//7v/9C1a1ezvNJb1ySeB7iIJTF+fn6WHsFmsa0MdpXBrvoSExNRWlqK8ePH6xaa3bt3B1C2GHrllVfw2GOPASg7oc/hw4exYcMGbNq0Se92/Pz8MHnyZKxbtw6xsbFo1aoVtFotdu7ciSVLlgAAfv/9d4SEhCAlJQVOTk66+9izZw92796te9WwMpMnT8aHH36oW8SGh4fj7Nmz+Prrrw1u36JFCyxYsED35xdeeAEHDx7Et99+a9Qi9qGHHtL78+effw4fHx/8+++/6NatG3bs2AGtVovPPvsMzs7O6Nq1K+Lj4/UW8Zs3b0a7du2wbt06AGUH2f/777/x9ttvV3v/APD111+jX79++Oijj3SXde3atcrrjBgxQm+hPGnSpBrfRlV8fHwAAN7e3la7m4HE8wB3J7iOp501v6ioKEuPYLPYVga7ymBXfT179sTIkSPRvXt3TJgwAVu2bEFmZiZycnKQkJCAwYMH620/ePBgXLx4scLtREVFoVevXujcuTN27NgBADhy5AhSUlIwYcIEAMD58+eRl5cHb29v3Zks3dzcEB0dXeHX9IY89thjiImJwalTpwCUvQrbp08fdOrUyeD2Go0Gq1atQvfu3eHl5QU3NzccPHgQsbGxRrW5fPkyJk6ciLZt28Ld3R3+/v4AoLv+xYsX0aNHDzg7O+uuc/Np6S9evIgBAwboXVbVqetvdubMGYwcOdLo7QFUOOV9+Sux9D8SzwNcxJIYnrpPDtvKYFcZ7KrP3t4ehw4dwv79+9GlSxd88MEH6NixI6Kjo2t0O+VdJ0+erFvE7tixA3fffTe8vb0BAHl5eWjWrBlCQ0P1PsLCwvDyyy9Xex9NmzbFiBEj9G5/8uTJlW7/7rvvYuPGjXjllVdw+PBhhIaGYvTo0SguLjbqaxozZgwyMjKwZcsWnD59GqdPnwYAo69vDqYcaeHm/T1dXFyq3N7Ozg6Kor8PdUlJSY3v15pIPA9wdwISExYWxr+8hLCtDHaVURddPV3VOLskUPQ+jJnBWCqVCoMHD8bgwYOxdOlStG7dGkFBQWjevDmOHz+OoUOH6rY9fvy4wV/Fl3edNGkSlixZgrNnz2L37t34+OOPddv06dMHSUlJcHBw0L2qWVOTJ0/GwoULMXHiRERFRel2dTDk+PHjePDBB/H4448DKHtHenh4OLp06VLt/aSnpyMsLAxbtmzBnXfeCaBsd4gbde7cGV999RUKCwt1r8aWv0p84zY///yz3mU3b1OV8u/FihUrjL7OzXr06FHlbfj4+CAxMVH355ycnCr/EVO+D6yp+9TWBxLPA3wllsS0bdvW0iPYLLaVwa4y6qKrnZ0K3m5OFv2wszPuUI2nT5/Gm2++iTNnziA2NhY//PADUlNT0blzZ7z88st4++23sWvXLoSFhWHRokUIDQ2t8OYl4H9d/f39MWjQIDz55JPQaDR44IEHdNsEBgZi4MCBGDt2LH799VfExMTgxIkTeO2113DmzBmj5h0/fjxyc3Mxa9YsDB8+HM2bN6902w4dOuDQoUM4ceIELl68iGeeeQbJyclG3Y+npye8vb3xn//8BxEREfjtt98wf/58vW0mTZoElUqFmTNn4t9//8W+ffuwdu1avW2effZZXL58GS+//DLCwsKwY8cObNu2zagZAGDVqlX4448/8Nxzz+Gvv/7CpUuXsHnzZqSlpRl9G4sXL67yNkaMGIGvvvoKx44dw99//41p06ZVeZSkJk2awMXFBQcOHEBycjKys7ONnqW+kHge4CKWxMTFxdXq+oqioFhTbNRHibYGv4ZRFEBTd7+aklDbtmQYu8pgV33u7u44evQo7r33XgQEBGDJkiVYt24d7rnnHsyZMwfz58/HSy+9hO7du+PAgQP4+eefKxyZANDvOnnyZJw/fx7jxo3T+1W2SqXCvn37MGTIEMyYMQMBAQF47LHHcOXKFfj6+ho1b8OGDTFmzBicP3++yl0JAGDJkiXo06cPRo8ejWHDhqFp06YYO3asUfdjZ2eHnTt34uzZs+jWrRtefPFFvPvuu3rbuLm54b///S/+/vtv9O7dG6+99lqFN2y1atUK33//Pfbs2YOePXvi448/xptvvmnUDEDZrgC//vorzp8/j/79+2PgwIH46aef4OBg/C+vAwICqryNxYsXY+jQobj//vtx3333YezYsbrDlhni4OCA999/H5988gmaN2+OBx980OhZ6guJ5wGVcvNOGbeYnJwceHh4ID09vdoDGFPN5OTkmHw4DUVRsPWfrYjLrfmDfnH/xVDbV/JrPUUBjm8EMm/4tc097wAO1R8Wpz6pTVuqHLvKYFcZ7CqDXWUY6lq+BsvOzjapOV+JJTF5eXkmX7dEW2LSAtavoR8c7ao4HaCmWH8B69kGqGzBW4/Vpi1Vjl1lsKsMdpXBrjIkuvKNXdfxtLPmZ66zoC3ot6DqhekNHO0cjf9ejloNqN0AK/ze8wxzMthVBrvKMEfXrl276s7sdbNPPvmk2t0HaiI2NrbKN3j9+++/aNWqldnurzL33HMPjh07ZvBzr776Kp588knxGW5FEs8DXMRexydZ8zPXGUUc7Rwr3z2gNuzVVrmABczXlvSxqwx2lWGOrvv27av00E7G7jNrrObNmyM0NLTKz9eFTz/9FNeuXTP4OS8vL76oJUTieYCLWBKTnZ0NT09PS49hk9hWBrvKYFcZ5uhafsawuuDg4ID27dvX2f1VpkWLFlV+PiYmho9XARLPA9wnlsRY66nxrAHbymBXGewqg11lsKsMia5cxF7H086aX2X7WVHtsa0MdpXBrjLYVQa7ypDoykUsieGZj+SwrQx2lcGuMthVBrvKkOjKRSyJCQsLs/QINottZbCrDHaVwa4y2FWGRFcuYklMfdiB31axrQx2lcGuMthVBrvKkOjKRSyJiYqKsvQINottZbCrDHY1n5iYGKhUKoSGhtaLrsHBwVCpVMjKyrL0KGZTH7raIomuXMSSmGbNmll6BJvFtjLYVQa7yqgPXQcNGoTExER4eHhYbAZzL6TrQ1dbJNGVi1gSY0v/Mq9v2FYGu8pgVxn1oatarUbTpk1t6gQB9aGrLZLoykUsiXF2drb0CDaLbWWwqwx2rWj37t3o3r07XFxc4O3tjcDAQOTn50Or1WLlypVo2bIlnJyc0KtXLxw4cMDgbajVarRs2RKbN2/Wu/zcuXOws7PTHdIoKysLTz31FHx8fODu7o4RI0bg/Pnz1c4YHh4OlUqFS5cu6V3+3nvvoV27dgAqvgqanp6OiRMnokWLFnB1dUX37t3xzTffGN1l2LBheOGFFzBv3jx4enrC19cXW7ZsQX5+PmbMmIGGDRuiffv22L9/P4Cy3SuGDx8OAPD09IRKpcL06dN1u13c/DFs2LBqZ+DjVYZEVy5ir3Nw4MnLzM2W/mVe37CtDHaVUWddFQUoLbLMh6IYPWZiYiImTpyIJ554AhcvXkRwcDDGjx8PRVGwceNGrFu3DmvXrsVff/2F0aNH44EHHsDly5cr3I69vT0mTpyIHTt26F2+fft2DB48WHc2rgkTJiAlJQX79+/H2bNn0adPH4wcORIZGRlVzhkQEIB+/fph+/btFW5/0qRJBq9TWFiIvn37Yu/evbhw4QKefvppTJkyBSEhIUb3+eKLL9C4cWOEhITghRdewKxZszBhwgQMGjQIf/75J0aNGoUpU6agoKAAfn5++P777wGUvfs9MTERGzduhJ+fHxITE3Uf586dg7e3N4YMGVLt/fN5QIZEV5Wi1OAnzwbl5OTAw8MD2dnZcHd3t/Q4NiUuLg5+fn4mXbdYU4w1IWsAAIv7L4ba3kznXC4tAvYvLPv/e94BHJzMc7t1rDZtqXLsKqPOut74813XavB88ueff6Jv376IiYmpcNrXFi1aYPbs2Xj11Vd1l/Xv3x+33XYbNm3ahJiYGLRp00a3KEtPT0efPn0QExODVq1aQavVolWrVliyZAmeffZZ/P7777jvvvuQkpICJ6f/zde+fXssXLgQTz/9dJWzbtiwAR9++CEiIiIAlL0627FjR1y8eBGdOnVCcHAwhg8fjszMTDRq1Mjgbdx///3o1KkT1q5dW22bYcOGQaPR4NixYwAAjUYDDw8PjB8/Hl9++SUAICkpCc2aNcPJkydx++23VztDYWEhhg0bBh8fH/z000+ws6v69Ts+D8gw1LW2azC+EktivL29LT2CzWJbGewqg1319ezZEyNHjkT37t0xYcIEbNmyBZmZmcjJyUFCQgIGDx6st/3gwYNx8eLFCrfj7e2NXr16oXPnzrpXY48cOYKUlBRMmDABAHD+/Hnk5eXB29sbbm5uuo/o6GhERkZWO+tjjz2GmJgYnDp1CkDZq7B9+vRBp06dDG6v0WiwatUqdO/eHV5eXnBzc8PBgwcRGxtrdJ8ePXro/t/e3h7e3t7o3r277jJfX18AQEpKilG398QTTyA3Nxc7duyodgEL8PEqRaIrf4d+HU87a37x8fEICAiw9Bg2iW1lsKuMOutqry57RdQSavDbInt7exw6dAgnTpzAr7/+ig8++ACvvfYaDh06VKO7LO86efJk7NixA4sWLcKOHTtw99136xYMeXl5aNasGYKDgytcv7JXTm/UtGlTjBgxAjt27MDtt9+OHTt2YNasWZVu/+6772Ljxo3YsGEDunfvjgYNGmDevHkoLi42+utydHTU+7NKpdK7rPzX0lqtttrbWr16NQ4ePIiQkBA0bNjQqPvn84AMia5cxJKYDh061Pg6iqKgRFuCEm2JaXeqKICmiifLqj5nRUxpS9VjVxl11lWlsppdhFQqFQYPHozBgwdj6dKlaN26NYKCgtC8eXMcP34cQ4cO1W17/Phx9O/fv8JtlHedNGkSlixZgrNnz2L37t34+OOPddv06dMHSUlJcHBwgL+/v0mzTp48GQsXLsTEiRMRFRWFxx57rNJtjx8/jgcffBCPP/44gLKFZnh4OLp06WLSfRtDrS77B4RGo9G7/Pvvv8fKlSuxf/9+3RvRjMHnARkSXbmIJTHl+04ZS1EUbP1nK+Jy40y7Q0UBjm8EMqNNu74VqWlbMg67ymBXfadPn0ZQUBBGjRqFJk2a4PTp00hNTUXnzp3x8ssvY9myZWjXrh169eqFrVu3IjQ0tMKbq4D/dfX398egQYPw5JNPQqPR4IEHHtBtExgYiIEDB2Ls2LF45513EBAQgISEBOzduxfjxo1Dv379qp13/PjxmDVrFmbNmoXhw4ejefPmlW7boUMH7N69GydOnICnpyfWr1+P5ORk0UVs69atoVKp8Msvv+Dee++Fi4sLYmJiMHXqVLzyyivo2rUrkpKSAJQteL28vKq8PT5eZUh05T6xJKamD9YSbUmFBaxfQz842jlWco2baIqNX8B6tqnRr//qGz7BymBXGeyqz93dHUePHsW9996LgIAALFmyBOvWrcM999yDOXPmYP78+XjppZfQvXt3HDhwAD///LPBV7Fu7Dp58mScP38e48aNg4uLi+5ylUqFffv2YciQIZgxYwYCAgLw2GOP4cqVK7p9S6vTsGFDjBkzBufPn8fkyZOr3HbJkiXo06cPRo8ejWHDhqFp06YYO3ascWFM1KJFC6xYsQKLFi2Cr68vnn/+eZw5cwYFBQVYvXo1mjVrpvsYP358tbfHx6sMia48OsH1d8alp6dX+68zqpmwsLAaPWhvPCLBgn4L4GjnCEc7R+MPy3HjO5NHra56kWqvLvvVo5WqaVsyDrvKYFcZ7CqDXWUY6lrboxNwdwIS06pVK5Ov62jnWLvDatmrrWbfOFPUpi1Vjl1lsKsMdpXBrjIkunJ3AhKTmppq6RFsFtvKYFcZ7CrDHF27du2qd+itGz8M7YdbG7GxsZXel5ubW40OwyWJj1cZEl35SiyJcXNzs/QINottZbCrDHaVYY6u+/btQ0mJ4aPBGLvPrLGaN2+O0NDQKj9fH/DxKkOiKxex1/G0s+Z38+FOyHzYVga7ymBXGeboevMZwyQ5ODigffv2dXZ/puLjVYZEV+5OQGJqcnBrqhm2lcGuMthVBrvKYFcZEl25iCUxprzTkIzDtjLYVQa7ymBXGewqQ6IrF7HX8dcH5pecnGzpEWwW28pgVxnsKoNdZbCrDImuXMRed4sfLleEqac4pOqxrQx2lcGuMthVBrvKkOjKRSyJiYyMtPQINottZbCrDHaVwa4y2FWGRFcuYkkMz3gih21lsKsMdjWfmJgYqFQqhIaG1ouuwcHBUKlUyMrKsvQoVVq+fDl69epV5Tblba9du2b07W7btg2NGjWq3XD17L6mT58ucqpgiccrF7EkJiwszNIj2Cy2lcGuMthVRn3oOmjQICQmJsLDw8PSo9SIoYWan58fEhMT4ejoaJmhqvHoo48iPDxc/H42btyIbdu26f48bNgwzJs3r9a3K/F45cFRSQz3K5LDtjLYVQa7yqgPXdVqNZo2bWrpMczC3t4eTZs2haenp6VHMcjFxQUuLi6Vfr64uBhqdS1O136d1D9IuE8sWZXExERLj2Cz2FYGu8pg14p2796N7t27w8XFBd7e3ggMDER+fj60Wi1WrlyJli1bwsnJCb169cKBAwcM3sbVq1fRsmVLbN68We/yc+fOwc7ODleuXAEAZGVl4amnnoKPjw/c3d0xYsQInD9/vtoZw8PDoVKpcOnSJb3L33vvPbRr1w5Axd0J0tPTMXHiRLRo0QKurq7o3r07vvnmG6O7DBs2DC+88ALmzZsHT09P+Pr6YsuWLcjPz8eMGTPQsGFDtG/fHvv379ddx9Cv2ffs2QOVSmXwPpYvX44vvvgCP/30E1QqFVQqFYKDg3W7EwQFBel9bXv37kWPHj3g7OyM22+/HRcuXKjya/jpp5/Qp08fODs7o23btlixYgVKS0uN+vqzsrLwzDPPwNfXF87OzujWrRt++eUXg19n+S4Sn376Kdq0aQNnZ+dqb8PQbhUbNmzQW2De+Cr19OnTceTIEWzcuFHXKiYmBpmZmZg8eTJ8fHzg4uKCDh06YOvWrVV+bRLPA1zEkpi62k/oVsS2MthVRl11VRQFxZpii3zU5Ag3iYmJmDhxIp544glcvHgRwcHBGD9+PBRFwcaNG7Fu3TqsXbsWf/31F0aPHo0HHngAly9frnA7Xl5emDhxInbs2KF3+fbt2zF48GDd2bgmTJiAlJQU7N+/H2fPnkWfPn0wcuRIZGRkVDlnQEAA+vXrh+3bt1e4/UmTJhm8TmFhIfr27Yu9e/fiwoULePrppzFlyhSEhIQY3eeLL75A48aNERISghdeeAGzZs3ChAkTMGjQIPz5558YNWoUpkyZgoKCAqNv80YLFizAI488grvvvhuJiYlITEzEoEGDdJ9v2LCh3vYvv/wy1q1bhz/++AM+Pj4YM2ZMpafqPXbsGKZOnYq5c+fi33//xSeffIJt27bhjTfeqHYurVaLe+65B8ePH8fXX3+Nf//9F2+99Rbs7e0rvU5ERAS+//57/PDDDwgNDTXpNqqyceNGDBw4EDNnztS18vPzw+uvv45///0X+/fvx8WLF7F582Y0bty4ytuSeB7g7gTX8bSz5ldYWGjpEWwW28pgVxl11bVEW4I1IWvq5L5utrj/YqjtjftVbmJiIkpLSzF+/HjdQrN79+4AgLVr1+KVV17BY489BgB4++23cfjwYWzYsAGbNm3Su53CwkJMnjwZ69atQ2xsLFq1agWtVoudO3diyZIlAIDff/8dISEhSElJgZOTk+4+9uzZg927d+Ppp5+uctbJkyfjww8/xKpVqwCUvTp79uxZfP311wa3b9GiBRYsWKD78wsvvICDBw/i22+/Rf/+/Y3q07NnT938ixcvxltvvYXGjRtj5syZAIClS5di8+bN+Ouvv3D77bcbdZs3cnNzg4uLC4qKigzuClFUVKT352XLluGuu+4CULbAbtmyJX788Uc88sgjFa67YsUKLFq0CNOmTQMAtG3bFqtWrcLChQuxbNmyKuf6v//7P4SEhODixYsICAjQXb8qxcXF+PLLL+Hj4wMA+PXXX2t8G1Xx8PCAWq2Gq6urXqvY2Fj07t0b/fr1A2DcrgISzwN8JZbE1OSVCUVRUKI1/C9bKApQWlT9h+bWOVUgj2ssg11lsKu+nj17YuTIkejevTsmTJiALVu2IDMzEzk5OUhISMDgwYP1th88eDAuXrxY4XYURUGvXr3QuXNn3auxR44cQUpKCiZMmAAAOH/+PPLy8uDt7Q03NzfdR3R0tFGHPHrssccQExODU6dOASh7FbZPnz7o1KmTwe01Gg1WrVqF7t27w8vLC25ubjh48CBiY2ON7tOjRw/d/9vb28Pb21u3yAcAX19fAEBKSorRt1kTNz9eBw4cqPt/Ly8vdOzY0eD3AyjrvXLlSr3W5a9iVvfKcWhoKFq2bKlbfBqjdevWugWsqbdhilmzZmHnzp3o1asXFi5ciBMnTlR7HYnnAb78SGJcXV2N2k5RFGz9ZyvicuMMfRI4vhHIjDbzdNbN2LZUM+wqo666Oto5YnH/xXVyX4bu21j29vY4dOgQTpw4gV9//RUffPABXnvtNRw6dKhG91nedfLkydixYwcWLVqEHTt24O6774a3tzcAIC8vD82aNUNwcHCF6xvz692mTZtixIgR2LFjB26//Xbs2LEDs2bNqnT7d999Fxs3bsSGDRvQvXt3NGjQAPPmzUNxsfEvMtx8dACVSqV3Wfm+rlqtFgBgZ2dXYYFU2a/7jVG+b6kp8vLysGLFCowfP77Gt1vVm7Yq06BBgxrdhrla3XPPPbhy5Qr27duHQ4cOYeTIkZg9ezbWrl1b6XUkngf4Sux1PO2s+aWnpxu1XYm2RG8B69fQ739/IWiKa76A9WwDGPlrPWtlbFuqGXaVUVddVSoV1PZqi3xU9iaiqmYdPHgwVqxYgXPnzkGtViMoKAjNmzfH8ePH9bY9fvw4unTpUuE2yrtOmjQJFy5cwNmzZ7F7925MnjxZt02fPn2QlJQEBwcHtG/fXu+jun0Yy02ePBm7du3CyZMnERUVpdvVwZDjx4/jwQcfxOOPP46ePXuibdu24oeF8vHxQW5uLvLz83WXhYaGVnkdtVpd6d/72dnZen8ufxUaADIzMxEeHo7OnTsbvG6fPn0QFhZWoXX79u1hZ1f1kqtHjx6Ij4+vVa/qbsPHxwdJSUl6C1lTW/n4+GDatGn4+uuvsWHDBvznP/+p8nYkngf4Sux1/HWX+bVs2bLG11nQbwFcHVwN/4UwarVxi1N7NVDDv1CsjSltqXrsKoNd9Z0+fRpBQUEYNWoUmjRpgtOnTyM1NRWdO3fGyy+/jGXLlqFdu3bo1asXtm7ditDQ0ApvrgL+19Xf3x+DBg3Ck08+CY1GgwceeEC3TWBgIAYOHIixY8finXfeQUBAABISErB3716MGzdOt09jVcaPH49Zs2Zh1qxZGD58OJo3b17pth06dMDu3btx4sQJeHp6Yv369UhOTja4CDeXAQMGwNXVFa+++irmzJmD06dP6x3n1BB/f38cPHgQYWFh8Pb21jusVPnuCuVWrlwJb29v+Pr64rXXXkPjxo0rPRnA0qVLcf/996NVq1Z4+OGHYWdnh/Pnz+PChQtYvXp1lTMNHToUQ4YMwUMPPYT169ejffv2uHTpElQqFe6++26jWlR3G8OGDUNqaireeecdPPzwwzhw4AD2798Pd3f3KludPn0aMTExcHNzg5eXF5YvX46+ffuia9euKCoqwi+//FLpwr6cxPMAX4klMdHRNd8FwNHOsfJXNOzVgINT9R82voAFTGtL1WNXGeyqz93dHUePHsW9996LgIAALFmyBOvWrcM999yDOXPmYP78+XjppZfQvXt3HDhwAD///DM6dOhQ4XZu7Dp58mScP38e48aN0/uVskqlwr59+zBkyBDMmDEDAQEBeOyxx3DlypUKi7XKNGzYEGPGjMH58+f1XuU1ZMmSJejTpw9Gjx6NYcOGoWnTpiJnf7qRl5cXvv76a+zbt093SK/ly5dXeZ2ZM2eiY8eO6NevH3x8fPRe/b569aretm+99Rbmzp2Lvn37IikpCf/9738rPR7r6NGj8csvv+DXX3/Fbbfdhttvvx3vvfee7g181fn+++9x2223YeLEiejSpQsWLlxY498UV3UbnTt3xkcffYRNmzahZ8+eCAkJ0XsjniELFiyAvb09unTpAh8fH8TGxkKtVmPx4sXo0aMHhgwZAnt7e+zcubPK25F4HlApt/hLkDk5OfDw8EB6ejq8vLwsPc4tqVhTrHtHcYV3+JYWAfsXlv3/Pe+ULVKJiIiEBQcHY/jw4cjMzOTh94SUr8Gys7OrfDW4MnwllsTUh1Mi2iq2lcGuMthVBrvKYFcZEl25iCUxtTk2HVWNbWWwqwx2lWGOrl27dtU7HNSNH4b2w62N2NjYSu/Lzc2tRofhkiT1eN2+fXulX3vXrl1F7rM+kejKN3aRmNjYWN2pCcm82FYGu8pgVxnm6Lpv375KD7Fk7D6zxmrevHmV74Sv6g1jdam867Bhw8z6pu8HHngAAwYMMPi5mw8rZosknge4iCUxNx6AmcyLbWWwqwx2lWGOrsa+4cgcyg/zVd9JPV4bNmxY4ZS2txKJrtyd4Dqedtb88vLyLD2CzWJbGewqg11lsKsMdpUh0ZWLWBLDfxjIYVsZ7CqDXWWwqwx2lSHRlYtYEnMr7ONjKWwrg11lsKsMdpXBrjIkunIRex1PO2t+OTk5lh7BZrGtDHaVwa4y2FUGu8qQ6MpF7HW3+DkfRDRp0sTSI9gstpXBrjLYVQa7ymBXGRJduYglMfXlmH+2iG1lsKsMdpXBrjLYVYZE13q5iN20aRP8/f3h7OyMAQMGICQkpMrtv/vuO3Tq1AnOzs7o3r079u3bV0eTUlU6duxo6RFsFtvKYFcZ7CqDXWWwqwyJrvVuEbtr1y7Mnz8fy5Ytw59//omePXti9OjRSElJMbj9iRMnMHHiRDz55JM4d+4cxo4di7Fjx+LChQt1PDndjKfuk8O2MthVBrvKYFcZ7CpDoqtKqWc7gw4YMAC33XYbPvzwQwCAVquFn58fXnjhBSxatKjC9o8++ijy8/Pxyy+/6C67/fbb0atXL3z88cfV3l9OTg48PDyQnp4OLy8v830hBI1GA3t7+2q3K9YUY03IGgDA4v6LobZX/++TpUXA/oVl/3/PO4CDk8SoVsfYtlQz7CqDXWWwqwx2lWGoa/kaLDs7G+7u7jW+zXp1MLTi4mKcPXsWixcv1l1mZ2eHwMBAnDx50uB1Tp48ifnz5+tdNnr0aOzZs6dm912Ui+IiHlbDnCIjI406xVyJtgTQXj86RGkRcOO/qzTFQtNZt6ioKHTo0MHSY9gcdpXBrjLYVQa7ypDoWq8WsWlpadBoNBXO1+zr64tLly4ZvE5SUpLB7ZOSkgxuX1RUhKKiIt2fs7OzAQDv7J4Gpwb8l5c5KVotVKdqtsdKTlwy1CpVJZ/M4Sux17m5ufEwMALYVQa7ymBXGewqw1DX8j+bulNAvVrE1oU1a9ZgxYoVFS5/b84RC0xDN3sLv1Xx2U/qbA4iIiKqG7m5ufDw8Kjx9erVIrZx48awt7dHcnKy3uXJyclo2rSpwes0bdq0RtsvXrxYb/eDrKwstG7dGrGxsSYFJMNycnLg5+eHuLg4k/ZzocqxrQx2lcGuMthVBrvKqKyroijIzc1F8+bNTbrderWIVavV6Nu3L4KCgjB27FgAZW/sCgoKwvPPP2/wOgMHDkRQUBDmzZunu+zQoUMYOHCgwe2dnJzg5FTxV9IeHh58wApwd3dnVyFsK4NdZbCrDHaVwa4yDHWtzQuI9WoRCwDz58/HtGnT0K9fP/Tv3x8bNmxAfn4+ZsyYAQCYOnUqWrRogTVryt7NPnfuXAwdOhTr1q3Dfffdh507d+LMmTP4z3/+Y8kvg4iIiIgE1btF7KOPPorU1FQsXboUSUlJ6NWrFw4cOKB781ZsbCzs7P73ZqFBgwZhx44dWLJkCV599VV06NABe/bsQbdu3Sz1JRARERGRsHq3iAWA559/vtLdB4KDgytcNmHCBEyYMMGk+3JycsKyZcsM7mJApmNXOWwrg11lsKsMdpXBrjKkuta7kx0QEREREVWn3p12loiIiIioOlzEEhEREZHV4SKWiIiIiKyOzS9ijx49ijFjxqB58+ZQqVTYs2dPtdcJDg5Gnz594OTkhPbt22Pbtm3ic1qbmnb94YcfcNddd8HHxwfu7u4YOHAgDh48WDfDWhFTHq/ljh8/DgcHB/Tq1UtsPmtlSteioiK89tpraN26NZycnODv74/PP/9cflgrYkrX7du3o2fPnnB1dUWzZs3wxBNPID09XX5YK7JmzRrcdtttaNiwIZo0aYKxY8ciLCys2ut999136NSpE5ydndG9e3fs27evDqa1HqZ03bJlC+688054enrC09MTgYGBCAkJqaOJrYepj9lyO3fuhEql0p0jwFg2v4jNz89Hz549sWnTJqO2j46Oxn333Yfhw4cjNDQU8+bNw1NPPcUF101q2vXo0aO46667sG/fPpw9exbDhw/HmDFjcO7cOeFJrUtNu5bLysrC1KlTMXLkSKHJrJspXR955BEEBQXhs88+Q1hYGL755ht07NhRcErrU9Oux48fx9SpU/Hkk0/in3/+wXfffYeQkBDMnDlTeFLrcuTIEcyePRunTp3CoUOHUFJSglGjRiE/P7/S65w4cQITJ07Ek08+iXPnzmHs2LEYO3YsLly4UIeT12+mdA0ODsbEiRNx+PBhnDx5En5+fhg1ahSuXr1ah5PXf6a0LRcTE4MFCxbgzjvvrPkdK7cQAMqPP/5Y5TYLFy5UunbtqnfZo48+qowePVpwMutmTFdDunTpoqxYscL8A9mImnR99NFHlSVLlijLli1TevbsKTqXtTOm6/79+xUPDw8lPT29boayAcZ0fffdd5W2bdvqXfb+++8rLVq0EJzM+qWkpCgAlCNHjlS6zSOPPKLcd999epcNGDBAeeaZZ6THs1rGdL1ZaWmp0rBhQ+WLL74QnMz6Gdu2tLRUGTRokPLpp58q06ZNUx588MEa3Y/NvxJbUydPnkRgYKDeZaNHj8bJkyctNJFt0mq1yM3NhZeXl6VHsXpbt25FVFQUli1bZulRbMbPP/+Mfv364Z133kGLFi0QEBCABQsW4Nq1a5YezaoNHDgQcXFx2LdvHxRFQXJyMnbv3o17773X0qPVa9nZ2QBQ5fMl/+6qOWO63qygoAAlJSX8u6saxrZduXIlmjRpgieffNKk+6mXJzuwpKSkJN3Zwcr5+voiJycH165dg4uLi4Umsy1r165FXl4eHnnkEUuPYtUuX76MRYsW4dixY3Bw4I+zuURFReH333+Hs7MzfvzxR6SlpeG5555Deno6tm7daunxrNbgwYOxfft2PProoygsLERpaSnGjBlT491nbiVarRbz5s3D4MGDqzwTZWV/dyUlJUmPaJWM7XqzV155Bc2bN6/wDwb6H2Pb/v777/jss88QGhpq8n3xlViqczt27MCKFSvw7bffokmTJpYex2ppNBpMmjQJK1asQEBAgKXHsSlarRYqlQrbt29H//79ce+992L9+vX44osv+GpsLfz777+YO3culi5dirNnz+LAgQOIiYnBs88+a+nR6q3Zs2fjwoUL2Llzp6VHsSmmdH3rrbewc+dO/Pjjj3B2dhaczroZ0zY3NxdTpkzBli1b0LhxY5Pviy/d3KRp06ZITk7Wuyw5ORnu7u58FdYMdu7ciaeeegrfffcd/yVbS7m5uThz5gzOnTunO02zVquFoihwcHDAr7/+ihEjRlh4SuvUrFkztGjRAh4eHrrLOnfuDEVREB8fjw4dOlhwOuu1Zs0aDB48GC+//DIAoEePHmjQoAHuvPNOrF69Gs2aNbPwhPXL888/j19++QVHjx5Fy5Ytq9y2sr+7mjZtKjmiVapJ13Jr167FW2+9hf/7v/9Djx49hCe0Xsa2jYyMRExMDMaMGaO7TKvVAgAcHBwQFhaGdu3aVXt/XMTeZODAgRUOS3Lo0CEMHDjQQhPZjm+++QZPPPEEdu7cifvuu8/S41g9d3d3/P3333qXffTRR/jtt9+we/dutGnTxkKTWb/Bgwfju+++Q15eHtzc3AAA4eHhsLOzM/ovPaqooKCgwm4v9vb2AACFZ0DXURQFL7zwAn788UcEBwcb9bM8cOBABAUFYd68ebrL+HeXPlO6AsA777yDN954AwcPHkS/fv2Ep7RONW3bqVOnCn9/LVmyBLm5udi4cSP8/PyMvmOblpubq5w7d045d+6cAkBZv369cu7cOeXKlSuKoijKokWLlClTpui2j4qKUlxdXZWXX35ZuXjxorJp0ybF3t5eOXDggKW+hHqppl23b9+uODg4KJs2bVISExN1H1lZWZb6Euqlmna9GY9OYFhNu+bm5iotW7ZUHn74YeWff/5Rjhw5onTo0EF56qmnLPUl1Es17bp161bFwcFB+eijj5TIyEjl999/V/r166f079/fUl9CvTRr1izFw8NDCQ4O1nu+LCgo0G0zZcoUZdGiRbo/Hz9+XHFwcFDWrl2rXLx4UVm2bJni6Oio/P3335b4EuolU7q+9dZbilqtVnbv3q13ndzcXEt8CfWWKW1vZsrRCWx+EXv48GEFQIWPadOmKYpSFm3o0KEVrtOrVy9FrVYrbdu2VbZu3Vrnc9d3Ne06dOjQKrenMqY8Xm/ERaxhpnS9ePGiEhgYqLi4uCgtW7ZU5s+fr/eETKZ1ff/995UuXbooLi4uSrNmzZTJkycr8fHxdT98PWaoKQC9v4uGDh1a4fnz22+/VQICAhS1Wq107dpV2bt3b90OXs+Z0rV169YGr7Ns2bI6n78+M/UxeyNTFrGq63dORERERGQ1eHQCIiIiIrI6XMQSERERkdXhIpaIiIiIrA4XsURERERkdbiIJSIiIiKrw0UsEREREVkdLmKJiIiIyOpwEUtEREREVoeLWCIzCQ4OhkqlQnBwsNHb7t69u9ptp0+fDn9//9oPaAR/f3/cf//91W5Xk6/VGF999RU6deoER0dHNGrUyCy3aU1q+/XHxMRApVJh7dq15h/OAvdjDlqtFt26dcMbb7yhu2zbtm1QqVSIiYnRXTZs2DAMGzas7ge0QuXf/23bttX4ugcOHICbmxtSU1PNPxjdsriIJQLw7bffQqVS4ccff6zwuZ49e0KlUuHw4cMVPteqVSsMGjSo0tvdsWMHNmzYYM5Ra+3ff//F8uXL9f4it6RLly5h+vTpaNeuHbZs2YL//Oc/Zr+Pffv2Yfny5Wa/XXOoyddfn78OSaZ83d988w3i4uLw/PPPywxFNXL33Xejffv2WLNmjaVHIRvCRSwRgDvuuAMA8Pvvv+tdnpOTgwsXLsDBwQHHjx/X+1xcXNz/t3fnQVEcbx/Av7u87C6wAgKLiOiGSy5BI0rigoAnIt6KUpoS8SxFEe8Y4wFeiWjhhUg8I6VVETUQjygaPBGJRuOBSlCRRLQ8UA5BQOB5//C3UwwLe4BGSfWnylJ6Zrr76emR3pmeXvzzzz/csb6+vnjz5g18fX25fd7HIHbbtm3Izs5uUh613b59G1FRUU0axNYXa2OdOXMGNTU12LBhA8aNG4eRI0c2Oc+6jh07hqioqPee7/ugS/yfchwfUmPijomJQUhICExMTNTul5qaitTU1KZUj9HSlClTkJCQgJKSko9dFeY/gg1iGQaAtbU1bG1tVQaxGRkZICIEBwerbFP+rBzECoVCSCQSCIXv97LS19eHWCx+r3k21fuM9dmzZwDQ7KYR1NTUoLy8vMn5NNf4P2XXrl3D9evXtfpAJBKJIBKJ/oVaNU5paenHrsJ7M3z4cFRUVCApKeljV4X5j2CDWIb5Hx8fH1y7dg1v3rzh0tLT0+Hm5obAwEBcunQJNTU1vG0CgQDe3t4AVOeJ+vv74+jRo8jLy4NAIIBAIFCZ21pTU4OVK1fCxsYGEokEvXr1wr1793j71J0TW3te4g8//AB7e3uIxWJ07doVly9fVhvj7t27ERwcDADo0aMHV6+6c1svXLgALy8vSCQS2NnZYc+ePbzt9c2JzcnJwfDhw2FlZQWJRAIbGxuEhISgqKiowfp89tlnWLp0KQBAJpNBIBBwj41TUlIQFBQEa2triMVi2NvbY/ny5aiurlbJJzMzE/3790fLli1hZGQEDw8PbNiwgWu/uLg4AODiFQgE3LGlpaWYM2cO2rZtC7FYDCcnJ6xduxZExCtDIBBg+vTp2Lt3L9zc3CAWi3H8+HE1rQ1s2bKF29fa2hrh4eEoLCzUKv66NMWhpE2fuHv3LkaMGAEzMzNIJBJ06dIFv/zyi9pY6oqNjYVcLoeBgQH8/Pxw69atRpXz9u1bREVFwdHRERKJBObm5vDx8cHJkyd1iru25ORkiEQirZ4U1J0Tq+zb+/fv13htAu/6Xr9+/WBiYgJDQ0P4+fmpPLXJy8vDtGnT4OTkBAMDA5ibmyM4OFjlaYhyzu7Zs2cxbdo0WFpawsbGRm39N23aBDc3NxgaGqJly5bo0qUL9u3bx9snPz8fEyZM4K4lW1tbTJ06FZWVlQCAly9fYu7cuXB3d4dUKoWxsTECAwNx/fp1je0HaN+fLC0t4eHhgZSUFK3yZRhN/u9jV4BhPhU+Pj5ITExEZmYm90stPT0dCoUCCoUCRUVFuHXrFjw8PLhtzs7OMDc3rze/RYsWoaioCI8ePUJsbCwAQCqV8vb57rvvIBQKMXfuXBQVFWHNmjUYM2YMMjMzNdZ33759KCkpwZQpUyAQCLBmzRoMGzYMDx48gL6+fr3H+Pr6IiIiAhs3bsQ333wDFxcXAOD+BoB79+5hxIgRmDBhAkJDQ7Fz506MGzcOnp6ecHNzqzffyspKBAQEoKKiAjNmzICVlRXy8/Nx5MgRFBYWNvhId/369dizZw9+/vlnxMfHQyqVcu27e/duSKVSzJ49G1KpFGlpaViyZAmKi4sRExPD5XHy5EkMGDAArVu3xsyZM2FlZYU7d+7gyJEjmDlzJqZMmYLHjx/j5MmTSExM5JVPRBg0aBBOnz6NCRMmoFOnTjhx4gTmzZuH/Px87rwppaWlYf/+/Zg+fTosLCzUvnC3bNkyREVFoXfv3pg6dSqys7MRHx+Py5cvIz09Hfr6+mrjr0tdHEra9ImsrCx4e3ujTZs2+Prrr2FkZIT9+/djyJAhOHjwIIYOHdpgTEp79uxBSUkJwsPDUV5ejg0bNqBnz564efMmWrVqpVM5y5Ytw+rVqzFx4kR4eXmhuLgYV65cwdWrV9GnTx+t4q7r4sWL6NChQ4PXgTa0uTbT0tIQGBgIT09PLF26FEKhELt27ULPnj1x/vx5eHl5AQAuX76MixcvIiQkBDY2Nnj48CHi4+Ph7++P27dvw9DQkFf2tGnTIJPJsGTJErV3Yrdt24aIiAiMGDECM2fORHl5OW7cuIHMzEyMHj0aAPD48WN4eXmhsLAQkydPhrOzM/Lz83HgwAGUlZVBJBLhwYMHSE5ORnBwMGxtbfH06VMkJCTAz88Pt2/fhrW1dYN10LU/eXp6Ijk5WdfTwTD1I4ZhiIgoKyuLANDy5cuJiOjt27dkZGREP/74IxERtWrViuLi4oiIqLi4mPT09GjSpEnc8adPnyYAdPr0aS4tKCiI5HK5SlnKfV1cXKiiooJL37BhAwGgmzdvcmmhoaG8PHJzcwkAmZub08uXL7n0lJQUAkCHDx9WG2dSUpJKPZXkcjkBoHPnznFpz549I7FYTHPmzGkw1mvXrhEASkpKUlt2fZYuXUoA6Pnz57z0srIylX2nTJlChoaGVF5eTkREVVVVZGtrS3K5nF69esXbt6amhvt3eHg41fffXXJyMgGgFStW8NJHjBhBAoGA7t27x6UBIKFQSFlZWRpjevbsGYlEIurbty9VV1dz6Zs3byYAtHPnTo3x16ehOHTpE7169SJ3d3euDYnetZVCoSBHR0e15SvLMTAwoEePHnHpmZmZBIBmzZqlczkdO3akoKCgRsXdEBsbGxo+fLhK+q5duwgA5ebmcml+fn7k5+fH/azttVlTU0OOjo4UEBDA62tlZWVka2tLffr04aXVlZGRQQBoz549KvXz8fGhqqoqjXEOHjyY3Nzc1O4zduxYEgqFdPnyZZVtynqXl5fz+inRu3MtFospOjqalwaAdu3axaXp2p9WrVpFAOjp06ca42MYTdh0Aob5HxcXF5ibm3NzXa9fv47S0lJu9QGFQsE9JszIyEB1dTU3H7axwsLCePPxunfvDgB48OCBxmNHjRqFli1bNupYdVxdXbm8gHePuZ2cnNTmq7zTeuLECZSVlTWpfCUDAwPu3yUlJXjx4gW6d++OsrIy3L17F8C7uY+5ubmIjIxUmVOq6ZEz8O6FIT09PURERPDS58yZAyLCr7/+ykv38/ODq6urxnxPnTqFyspKREZG8uYNT5o0CcbGxjh69KjGPBpDU594+fIl0tLSMHLkSK5NX7x4gYKCAgQEBCAnJwf5+fkayxkyZAjatGnD/ezl5YUvvvgCx44d07kcU1NTZGVlIScn5721Q0FBAa8dGkPTtfnnn38iJycHo0ePRkFBARdjaWkpevXqhXPnznHTj2r35bdv36KgoAAODg4wNTXF1atXVcqeNGkS9PT0NNbR1NQUjx49anAaUU1NDZKTkzFw4EB06dJFZbvyGhGLxVw/ra6uRkFBAaRSKZycnOqtn1Jj+pPyvLx48UJjfAyjCRvEMsz/CAQCKBQKbu5reno6LC0t4eDgAIA/iFX+3dRBbLt27Xg/K/+Df/Xq1Qc9Vpd8lXmry9fW1hazZ8/G9u3bYWFhgYCAAMTFxamdD6tJVlYWhg4dChMTExgbG0Mmk+Grr74CAC7f+/fvAwA6dOjQqDLy8vJgbW2NFi1a8NKV0yvy8vJ46ba2tlrnCwBOTk68dJFIBDs7O5V83xdNfeLevXsgIixevBgymYz3Rzk3V/mimTqOjo4qae3bt+fmeOpSTnR0NAoLC9G+fXu4u7tj3rx5uHHjRuMaoBaqM6dZV5raUjnoDg0NVYlx+/btqKio4PrpmzdvsGTJEm7etYWFBWQyGQoLC+u9RrTtZwsWLIBUKoWXlxccHR0RHh7Om4/7/PlzFBcXa7w+ampqEBsbC0dHR179bty4ofYabkx/Up4XbT5kMowmbE4sw9Ti4+ODw4cP4+bNm9x8WCWFQsHNlbxw4QKsra1hZ2fXpPIautuizS/gphz7IfJdt24dxo0bh5SUFKSmpiIiIgKrV6/GpUuXNL6cUldhYSH8/PxgbGyM6Oho2NvbQyKR4OrVq1iwYAHvBbt/U+07ap8iTedO2W5z585FQEBAvfsqP7Q1hS7l+Pr64v79+1y/2b59O2JjY7F161ZMnDixUeWbm5s3+cOctm0ZExODTp061buvcg78jBkzsGvXLkRGRqJbt24wMTGBQCBASEhIvX1Z237m4uKC7OxsHDlyBMePH8fBgwexZcsWLFmyRKclyVatWoXFixdj/PjxWL58OczMzCAUChEZGan2WmtMf1KeFwsLC63rxzANYYNYhqml9nqx6enpiIyM5LZ5enpCLBbjzJkz3NvwmnyKdxs+ZJ3c3d3h7u6Ob7/9FhcvXoS3tze2bt2KFStW6JTPmTNnUFBQgEOHDvHeMM/NzeXtZ29vDwC4desWevfu3WB+DcUsl8tx6tQplJSU8O7GKqcryOVynepdO18AyM7O5n3QqaysRG5urtq6qtPUc6esi76+fqPrAKDeR/9//fUX96KbruWYmZkhLCwMYWFheP36NXx9fbFs2TJuEKtr3M7Ozip95X1T9j1jY2ONMR44cAChoaFYt24dl1ZeXs5bqaKxjIyMMGrUKIwaNQqVlZUYNmwYVq5ciYULF0Imk8HY2LjelSPq1q9Hjx7YsWMHL72wsFDtYLMx/Sk3N5e708swTcWmEzBMLV26dIFEIsHevXuRn5/PuxMrFovRuXNnxMXFobS0VKupBEZGRk16pP4hGBkZAcB7+QWqVFxcjKqqKl6au7s7hEIhKioqdM5PeRes9t3fyspKbNmyhbdf586dYWtri/Xr16vEU/vYhmLu378/qqursXnzZl56bGwsBAIBAgMDda47APTu3RsikQgbN27k1WPHjh0oKipCUFBQo/Jt6rmztLSEv78/EhIS8OTJE5Xt2n4laHJyMm+u4++//47MzEyuvXQpp6CggLdNKpXCwcGB1290jbtbt264detWo/qetjw9PWFvb4+1a9fi9evXKttrx6inp6fyJGPTpk31Lheni7ptJxKJ4OrqCiLC27dvIRQKMWTIEBw+fBhXrlxROV5Zp/rql5SUpHF+dGP60x9//IFu3bppjI1htMHuxDJMLSKRCF27dsX58+chFovh6enJ265QKLi7KdoMYj09PfHTTz9h9uzZ6Nq1K6RSKQYOHPhB6q6tTp06QU9PD99//z2KioogFovRs2dPWFpaNjrPtLQ0TJ8+HcHBwWjfvj2qqqqQmJgIPT09DB8+XOf8FAoFWrZsidDQUEREREAgECAxMVHlF61QKER8fDwGDhyITp06ISwsDK1bt8bdu3eRlZWFEydOAAB3HiMiIhAQEAA9PT2EhIRg4MCB6NGjBxYtWoSHDx+iY8eOSE1NRUpKCiIjI7m7bbqSyWRYuHAhoqKi0K9fPwwaNAjZ2dnYsmULunbtys3t1VVDcegiLi4OPj4+cHd3x6RJk2BnZ4enT58iIyMDjx490mptUAcHB/j4+GDq1KmoqKjA+vXrYW5ujvnz5+tcjqurK/z9/eHp6QkzMzNcuXIFBw4c4H1drK5xDx48GMuXL8fZs2fRt29fndpHW0KhENu3b0dgYCDc3NwQFhaGNm3aID8/H6dPn4axsTEOHz4MABgwYAASExNhYmICV1dXZGRk4NSpUw0uz6etvn37wsrKCt7e3mjVqhXu3LmDzZs3IygoiHuysGrVKqSmpsLPzw+TJ0+Gi4sLnjx5gqSkJFy4cAGmpqYYMGAAoqOjERYWBoVCgZs3b2Lv3r1aTZfSpT89e/YMN27cQHh4eJPiZhjOv7waAsN88hYuXEgASKFQqGw7dOgQAaAWLVqoLIFT3xJbr1+/ptGjR5OpqSkB4JbKUu5bd0mq+pawaWiJrZiYGJX6AaClS5dqjHHbtm1kZ2dHenp6vDrL5fJ6lztqaBki5XEPHjyg8ePHk729PUkkEjIzM6MePXrQqVOnNNaloSWm0tPT6csvvyQDAwOytram+fPn04kTJ+pdHuzChQvUp08fatGiBRkZGZGHhwdt2rSJ215VVUUzZswgmUxGAoGAt1xTSUkJzZo1i6ytrUlfX58cHR0pJiaGt2wS0bu2DQ8P1xhPbZs3byZnZ2fS19enVq1a0dSpU1WWAtNlia2G4tC1T9y/f5/Gjh1LVlZWpK+vT23atKEBAwbQgQMH1JZfu5x169ZR27ZtSSwWU/fu3en69esq+2tTzooVK8jLy4tMTU3JwMCAnJ2daeXKlVRZWakxbnU8PDxowoQJvDRdltjS5tokere83LBhw8jc3JzEYjHJ5XIaOXIk/fbbb9w+r169orCwMLKwsCCpVEoBAQF09+5dksvlFBoaqlK/+pbDqk9CQgL5+vpyZdvb29O8efOoqKiIt19eXh6NHTuWZDIZicVisrOzo/DwcG4JsfLycpozZw61bt2aDAwMyNvbmzIyMlTapqE20LY/xcfHk6GhIRUXF2sVH8NoIiBq4lsgDMMwDPOJSUxMRHh4OP7++2/2lb6fiM8//xz+/v4qXyLCMI3F5sQyDMMw/zljxoxBu3btuK+sZT6u48ePIycnBwsXLvzYVWH+Q9idWIZhGIZhGKbZYXdiGYZhGIZhmGaHDWIZhmEYhmGYZocNYhmGYRiGYZhmhw1iGYZhGIZhmGaHDWIZhmEYhmGYZocNYhmGYRiGYZhmhw1iGYZhGIZhmGaHDWIZhmEYhmGYZocNYhmGYRiGYZhmhw1iGYZhGIZhmGaHDWIZhmEYhmGYZuf/AcH88NITAqh9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from performance_profile import plot_performance_profile\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Example usage:\n",
    "# ax = plot_performance(\n",
    "#     results,\n",
    "#     instance_column=\"instance_name\",\n",
    "#     strategy_column=\"strategy\",\n",
    "#     metric_column=\"objective\",\n",
    "#     direction=\"min\",\n",
    "#     title=\"Performance Profile on Objective\"\n",
    "# )\n",
    "# plt.show()\n",
    "# relative optimality gap\n",
    "t[\"Gap\"] = (t[\"objective\"] - t[\"bound\"]) / t[\"bound\"]\n",
    "ax = plt.figure(figsize=(7, 5)).gca()\n",
    "plot_performance_profile(\n",
    "    t,\n",
    "    instance_column=\"instance_name\",\n",
    "    strategy_column=\"model\",\n",
    "    metric_column=\"objective\",\n",
    "    direction=\"min\",\n",
    "    highlight_best=True,\n",
    "    ax=ax,\n",
    "    title=\"Performance Profile on Objective\",\n",
    ")\n",
    "plt.show()\n",
    "\n",
    "# lower bound profile\n",
    "ax = plt.figure(figsize=(7, 5)).gca()\n",
    "plot_performance_profile(\n",
    "    t,\n",
    "    instance_column=\"instance_name\",\n",
    "    strategy_column=\"model\",\n",
    "    metric_column=\"bound\",\n",
    "    direction=\"max\",\n",
    "    highlight_best=True,\n",
    "    ax=ax,\n",
    "    title=\"Performance Profile on Lower Bound\",\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mo312",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
